[Objectives]To investigate the effects of silicon fertilizer spraying on the growth,yield,quality,and overall benefits of rice cultivation.[Methods]A systematic experiment involving the single-spray multi-promotion te...[Objectives]To investigate the effects of silicon fertilizer spraying on the growth,yield,quality,and overall benefits of rice cultivation.[Methods]A systematic experiment involving the single-spray multi-promotion technology of silicon fertilizer via unmanned aerial vehicles(UAVs)was conducted in three representative rice-growing areas:Ma'an Town,Shuikou Subdistrict,and Luzhou Town.[Results]The spraying of silicon fertilizer markedly enhanced the root development of rice,resulting in increased tiller number,plant height,stem thickness,panicle length,and 1000-grain weight,thereby effectively improving both yield and quality.This treatment exerted six primary beneficial effects:promoting robust and stable seedling growth,enhancing stress resistance,reducing reliance on chemical fertilizers,improving quality,increasing economic benefits,and significantly advancing ecological and social benefits.[Conclusions]The application of silicon fertilizer through spraying is an effective agronomic practice that simultaneously promotes increased rice yield,improved quality,enhanced efficiency,and the sustainable development of resources and the environment.展开更多
Unmanned Aerial Vehicles(UAVs)are increasingly employed in traffic surveillance,urban planning,and infrastructure monitoring due to their cost-effectiveness,flexibility,and high-resolution imaging.However,vehicle dete...Unmanned Aerial Vehicles(UAVs)are increasingly employed in traffic surveillance,urban planning,and infrastructure monitoring due to their cost-effectiveness,flexibility,and high-resolution imaging.However,vehicle detection and classification in aerial imagery remain challenging due to scale variations from fluctuating UAV altitudes,frequent occlusions in dense traffic,and environmental noise,such as shadows and lighting inconsistencies.Traditional methods,including sliding-window searches and shallow learning techniques,struggle with computational inefficiency and robustness under dynamic conditions.To address these limitations,this study proposes a six-stage hierarchical framework integrating radiometric calibration,deep learning,and classical feature engineering.The workflow begins with radiometric calibration to normalize pixel intensities and mitigate sensor noise,followed by Conditional Random Field(CRF)segmentation to isolate vehicles.YOLOv9,equipped with a bi-directional feature pyramid network(BiFPN),ensures precise multi-scale object detection.Hybrid feature extraction employs Maximally Stable Extremal Regions(MSER)for stable contour detection,Binary Robust Independent Elementary Features(BRIEF)for texture encoding,and Affine-SIFT(ASIFT)for viewpoint invariance.Quadratic Discriminant Analysis(QDA)enhances feature discrimination,while a Probabilistic Neural Network(PNN)performs Bayesian probability-based classification.Tested on the Roundabout Aerial Imagery(15,474 images,985K instances)and AU-AIR(32,823 instances,7 classes)datasets,the model achieves state-of-the-art accuracy of 95.54%and 94.14%,respectively.Its superior performance in detecting small-scale vehicles and resolving occlusions highlights its potential for intelligent traffic systems.Future work will extend testing to nighttime and adverse weather conditions while optimizing real-time UAV inference.展开更多
The study investigates the potential of UAV-based remote sensing technique for monitoring of Norway spruce health condition in the affected forest areas.The objectives are:(1)to test the applicability of UAV visible a...The study investigates the potential of UAV-based remote sensing technique for monitoring of Norway spruce health condition in the affected forest areas.The objectives are:(1)to test the applicability of UAV visible an near-infrared(VNIR)and geometrical data based on Z values of point dense cloud(PDC)raster to separate forest species and dead trees in the study area;(2)to explore the relationship between UAV VNIR data and individual spruce health indicators from field sampling;and(3)to explore the possibility of the qualitative classification of spruce health indicators.Analysis based on NDVI and PDC raster was successfully applied for separation of spruce and silver fir,and for identification of dead tree category.Separation between common beech and fir was distinguished by the object-oriented image analysis.NDVI was able to identify the presence of key indicators of spruce health,such as mechanical damage on stems and stem resin exudation linked to honey fungus infestation,while stem damage by peeling was identified at the significance margin.The results contributed to improving separation of coniferous(spruce and fir)tree species based on VNIR and PDC raster UAV data,and newly demonstrated the potential of NDVI for qualitative classification of spruce trees.The proposed methodology can be applicable for monitoring of spruce health condition in the local forest sites.展开更多
Investigating and monitoring the area of cultivated land reclaimed from rural settlements is important to optimize rural land use and understand spatial patterns. Measuring cultivated land area is costly and inefficie...Investigating and monitoring the area of cultivated land reclaimed from rural settlements is important to optimize rural land use and understand spatial patterns. Measuring cultivated land area is costly and inefficient, however, as this land use type is often widely dispersed and scattered. A new method is therefore explored in this study that utilizes a Phantom2 Vision +(P2V), one kind of Dajiang(DJI) unmanned aerial vehicle(UAV). The method proposed here includes generating rural settlement images using a P2V UAV, subsequently correcting them using a camera lens model, matching them with geo-coded high resolution alternatives, mosaicking them, measuring the area of cultivated land reclaimed from rural settlements, evaluating measurement accuracy, and analyzing overall efficiency.The results of this study show that use of a P2V UAV is reasonable in price, less than 8000 yuan(RMB), and that this method is able to measure cultivated land area reclaimed from rural settlements with 99% accuracy. This method is therefore low cost, highly efficient, and low risk, as well as being easy to learn and use. This UAV-based approach is also likely to be easily popularized and be particularly useful both for application across plains and flats as well as over mountains and hills. The method proposed in this study is also likely to prove beneficial for monitoring and managing rural land use and future consolidation.展开更多
This paper describes the general optimization design method of Solar-Powered Unmanned Aerial Vehicle which priority considering propulsion system planning. Based on the traditional solar powered aircraft design method...This paper describes the general optimization design method of Solar-Powered Unmanned Aerial Vehicle which priority considering propulsion system planning. Based on the traditional solar powered aircraft design method, the propulsion system top-level target parameters which affect the path planning are integrated into the general optimization design. According to the typical mission requirements of Solar-Powered Unmanned Aerial Vehicle, considering the design variables such as wing area, aspect ratio, design mission date and so on, the general optimization is carried out with the minimum aircraft weight as the optimization objective. The influence of wing area and aspect ratio on the optimal design results is analyzed and compared with the traditional design method. The results show that the general design method of Solar-Powered Unmanned Aerial Vehicle for priority considering propulsion system can greatly reduce the electricity demand of energy storage battery, greatly reduce the aircraft weight of Solar-Powered Unmanned Aerial Vehicle.展开更多
BACKGROUND:Rapid on-site triage is critical after mass-casualty incidents(MCIs)and other mass injury events.Unmanned aerial vehicles(UAVs)have been used in MCIs to search and rescue wounded individuals,but they mainly...BACKGROUND:Rapid on-site triage is critical after mass-casualty incidents(MCIs)and other mass injury events.Unmanned aerial vehicles(UAVs)have been used in MCIs to search and rescue wounded individuals,but they mainly depend on the UAV operator’s experience.We used UAVs and artificial intelligence(AI)to provide a new technique for the triage of MCIs and more efficient solutions for emergency rescue.METHODS:This was a preliminary experimental study.We developed an intelligent triage system based on two AI algorithms,namely OpenPose and YOLO.Volunteers were recruited to simulate the MCI scene and triage,combined with UAV and Fifth Generation(5G)Mobile Communication Technology real-time transmission technique,to achieve triage in the simulated MCI scene.RESULTS:Seven postures were designed and recognized to achieve brief but meaningful triage in MCIs.Eight volunteers participated in the MCI simulation scenario.The results of simulation scenarios showed that the proposed method was feasible in tasks of triage for MCIs.CONCLUSION:The proposed technique may provide an alternative technique for the triage of MCIs and is an innovative method in emergency rescue.展开更多
Background:Current automated forest investigation is facing a dilemma over how to achieve high tree-and plotlevel completeness while maintaining a high cost and labor efficiency.This study tackles the challenge by exp...Background:Current automated forest investigation is facing a dilemma over how to achieve high tree-and plotlevel completeness while maintaining a high cost and labor efficiency.This study tackles the challenge by exploring a new concept that enables an efficient fusion of aerial and terrestrial perspectives for digitizing and characterizing individual trees in forests through an Unmanned Aerial Vehicle(UAV)that flies above and under canopies in a single operation.The advantage of such concept is that the aerial perspective from the above-canopy UAV and the terrestrial perspective from the under-canopy UAV can be seamlessly integrated in one flight,thus grants the access to simultaneous high completeness,high efficiency,and low cost.Results:In the experiment,an approximately 0.5 ha forest was covered in ca.10 min from takeoff to landing.The GNSS-IMU based positioning supports a geometric accuracy of the produced point cloud that is equivalent to that of the mobile mapping systems,which leads to a 2–4 cm RMSE of the diameter at the breast height estimates,and a 4–7 cm RMSE of the stem curve estimates.Conclusions:Results of the experiment suggested that the integrated flight is capable of combining the high completeness of upper canopies from the above-canopy perspective and the high completeness of stems from the terrestrial perspective.Thus,it is a solution to combine the advantages of the terrestrial static,the mobile,and the above-canopy UAV observations,which is a promising step forward to achieve a fully autonomous in situ forest inventory.Future studies should be aimed to further improve the platform positioning,and to automatize the UAV operation.展开更多
This research was aimed at the defects in traditional artificial spraying control method and the problems such as the difficulty in pesticides applying,labor shortage and low operating efficiency in the middle and lat...This research was aimed at the defects in traditional artificial spraying control method and the problems such as the difficulty in pesticides applying,labor shortage and low operating efficiency in the middle and late stage of sugarcane high stalk crops.The aerial pesticide application technology for sugarcane main diseases and pests was systematically developed and demonstrated from the aspects of aircraft type choice,selection of special pesticides and auxiliaries,integration of pesticides and equipment,field operation,technical specifications,and large-scale application organization mode.The UAV model and flight technical parameters suitable for the sugarcane planting area in low-latitude plateau were analyzed,and the optimal agent formulation combination and application technology of the UAV flight control were screened out,and the UAV flight control was applied to the major sugarcane pests and diseases control in the low-latitude plateau in large scale(UAV flight control was popularized and applied to 15 527 hm 2 in 2018).The research results provided mature whole-process technical support for the normalization of the application of the UVA flight control of major sugarcane pests and diseases.The UAV control technology for major sugarcane pests and diseases had the advantages of ultra-low pesticides applying dosage and high operating efficiency,and could effectively solve the problems such as the difficulty in pesticides applying,labor shortage and low operating efficiency in the middle late growth stage of high stalk crops.This technology successfully opened up a simple,efficient and new way for the effective control of major sugarcane pests and diseases,and practically accelerated the process of integrated control and prevention of sugarcane pests and diseases.In addition,this technology had an extremely significant effect on reducing the loss of sugarcane farmers and enterprises caused by the epidemic and outbreak of sugarcane pests and diseases,increasing sugarcane yield and sugar content.At the same time,this technology played an important role in realizing the whole-process precise control of sugarcane pests and diseases,improving the quality and increasing the efficiency of sugarcane,and guaranteeing the national sugar safety.展开更多
Unmanned aerial vehicle technology was used to survey the vegetation coverage of typical urban-rural fringe, and descriptive statistics and geostatistical methods were used to analyze the urban-rural fringe of spatial...Unmanned aerial vehicle technology was used to survey the vegetation coverage of typical urban-rural fringe, and descriptive statistics and geostatistical methods were used to analyze the urban-rural fringe of spatial heterogeneity of vegetation coverage. The results showed that vegetation coverage in the study area was 27.2176% with the coefficient of variation of 31.7786%; that the vegetation coverage in separation distance of 〈0.18＇ showed positive spatial correlation, and the spatial correlation of vegetation coverage in separation distance of 〈0.18＇ was greater than that in 〉0.18＇; that the best fitting model for Semivariance function was exponential model with spatial variation ratio 0.726, which showed strong spatial correlation, and the spatial correlated scale was 0.18＇; that the vegetation coverage data in the study area was relatively stable, and the instability mainly occurred on the border of the study area and the surroundings.展开更多
Close-range sensing has yet to attain the status of being a dependable source for in situ forest information as the conventional field inventory.Each solution has its advantages and disadvantages in terms of accuracy,...Close-range sensing has yet to attain the status of being a dependable source for in situ forest information as the conventional field inventory.Each solution has its advantages and disadvantages in terms of accuracy,completeness,and efficiency.For a forest area,Terrestrial Laser Scanning(TLS)has the highest data quality,but is limited to static perspectives and lack the efficiency.Mobile Mapping Systems(MMS)systems gain on the efficiency but compromise the data quality.More recently,under-canopy UAV caught attentions for its potential to leverage the advantages of both TLS and MMS systems.This study demonstrates the feasibility of autonomous forest in situ investigation using an autonomous under-canopy UAV Laser Scanning(ULS)system,and evaluates the performance of such system in deriving key forest and tree attributes through a comparison with other close-range sensing systems such as the TLS and the Personal Laser Scanning(PLS).The under-canopy ULS system uses an onboard LiDAR sensor to aid its self-traverse in an unknown forest environment and to collect point cloud data during its movement inside the forest.Key factors influencing the systems’overall performance were investigated via various experiments.The point cloud data collected by the under canopy autonomous ULS system deliver similar stem capturing capacity as TLS in single layer forest stands with less undergrowth.The RMSEs of the DBH estimates were 0.81 cm(3.80%),4.12cm(19.92%),and 5.13cm(22.01%),respectively.The RMSEs of the stem curve estimates were 1.27 cm(5.48%),3.97 cm(17.63%),and 5.18 cm(22.49%),respectively.The geometric accuracy and the completeness of the point cloud significantly improved when the trajectory was densified.More studies on autonomous route planning in complex unknown forest is required to improve the system mobility,data quality,and the applicability of such systems in future practical forest in situ observations.展开更多
Numerous coal fires burn underneath the Datong coalfield because of indiscriminate mining.Landsat TM/ETM,unmanned aerial vehicle（UAV）,and infrared thermal imager were employed to monitor underground coal fires in th...Numerous coal fires burn underneath the Datong coalfield because of indiscriminate mining.Landsat TM/ETM,unmanned aerial vehicle（UAV）,and infrared thermal imager were employed to monitor underground coal fires in the Majiliang mining area.The thermal field distributions of this area in 2000,2002,2006,2007,and 2009 were obtained using Landsat TM/ETM.The changes in the distribution were then analyzed to approximate the locations of the coal fires.Through UAV imagery employed at a very high resolution（0.2 m）,the texture information,linear features,and brightness of the ground fissures in the coal fire area were determined.All these data were combined to build a knowledge model of determining fissures and were used to support underground coal fire detection.An infrared thermal imager was used to map the thermal field distribution of areas where coal fire is serious.Results were analyzed to identify the hot spot trend and the depth of the burning point.展开更多
At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture(Sichuan Province, Southwest China). On June 25, aerial images were acquired from a...At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture(Sichuan Province, Southwest China). On June 25, aerial images were acquired from an unmanned aerial vehicle(UAV), and a digital elevation model(DEM) was processed. Landslide geometrical features were then analyzed. These are the front and rear edge elevation, accumulation area and horizontal sliding distance. Then, the volume and the spatial distribution of the thickness of the deposit were calculated from the difference between the DEM available before the landslide, and the UAV-derived DEM collected after the landslide. Also, the disaster was assessed using high-resolution satellite images acquired before the landslide. These include Quick Bird, Pleiades-1 and GF-2 images with spatial resolutions of 0.65 m, 0.70 m, and 0.80 m, respectively, and the aerial images acquired from the UAV after the landslide with a spatial resolution of 0.1 m. According to the analysis, the area of the landslide was 1.62 km2, and the volume of the landslide was 7.70 ± 1.46 million m3. The average thickness of the landslide accumulation was approximately 8 m. The landslide destroyed a total of 103 buildings. The area of destroyed farmlands was 2.53 ha, and the orchard area was reduced by 28.67 ha. A 2-km section of Songpinggou River was blocked and a 2.1-km section of township road No. 104 was buried. Constrained by the terrain conditions, densely populated and more economically developed areas in the upper reaches of the Minjiang River basin are mainly located in the bottom of the valleys. This is a dangerous area regarding landslide, debris flow and flash flood events Therefore, in mountainous, high-risk disaster areas, it is important to carefully select residential sites to avoid a large number of casualties.展开更多
Since 2007,the Yellow Sea green tide has broken out every summer,causing great harm to the environment and society.Although satellite remote sensing(RS)has been used in biomass research,there are several shortcomings,...Since 2007,the Yellow Sea green tide has broken out every summer,causing great harm to the environment and society.Although satellite remote sensing(RS)has been used in biomass research,there are several shortcomings,such as mixed pixels,atmospheric interference,and difficult field validation.The biomass of green tide has been lacking a high-precision estimation method.In this study,high-resolution unmanned aerial vehicle(UAV)RS was used to quantitatively map the biomass of green tides.By utilizing experimental data from previous studies,a robust relationship was established to link biomass to the red-green-blue floating algae index(RGB-FAI).Then,the lab-based model for green tide biomass from visible images taken by the UAV camera was developed and validated by field measurements.Re sults show that the accurate and cost-effective method is able to estimate the green tide biomass and its changes in given local waters of the near and far seas.The study provided an effective complement to the traditional satellite RS,as well as high-precision quantitative techniques for decision-making in disaster management.展开更多
Latest advancements in the integration of camera sensors paves a way for newUnmannedAerialVehicles(UAVs)applications such as analyzing geographical(spatial)variations of earth science in mitigating harmful environment...Latest advancements in the integration of camera sensors paves a way for newUnmannedAerialVehicles(UAVs)applications such as analyzing geographical(spatial)variations of earth science in mitigating harmful environmental impacts and climate change.UAVs have achieved significant attention as a remote sensing environment,which captures high-resolution images from different scenes such as land,forest fire,flooding threats,road collision,landslides,and so on to enhance data analysis and decision making.Dynamic scene classification has attracted much attention in the examination of earth data captured by UAVs.This paper proposes a new multi-modal fusion based earth data classification(MMF-EDC)model.The MMF-EDC technique aims to identify the patterns that exist in the earth data and classifies them into appropriate class labels.The MMF-EDC technique involves a fusion of histogram of gradients(HOG),local binary patterns(LBP),and residual network(ResNet)models.This fusion process integrates many feature vectors and an entropy based fusion process is carried out to enhance the classification performance.In addition,the quantum artificial flora optimization(QAFO)algorithm is applied as a hyperparameter optimization technique.The AFO algorithm is inspired by the reproduction and the migration of flora helps to decide the optimal parameters of the ResNet model namely learning rate,number of hidden layers,and their number of neurons.Besides,Variational Autoencoder(VAE)based classification model is applied to assign appropriate class labels for a useful set of feature vectors.The proposedMMF-EDCmodel has been tested using UCM and WHU-RS datasets.The proposed MMFEDC model attains exhibits promising classification results on the applied remote sensing images with the accuracy of 0.989 and 0.994 on the test UCM and WHU-RS dataset respectively.展开更多
Remote sensing images carry crucial ground information,often involving the spatial distribution and spatiotemporal changes of surface elements.To safeguard this sensitive data,image encryption technology is essential....Remote sensing images carry crucial ground information,often involving the spatial distribution and spatiotemporal changes of surface elements.To safeguard this sensitive data,image encryption technology is essential.In this paper,a novel Fibonacci sine exponential map is designed,the hyperchaotic performance of which is particularly suitable for image encryption algorithms.An encryption algorithm tailored for handling the multi-band attributes of remote sensing images is proposed.The algorithm combines a three-dimensional synchronized scrambled diffusion operation with chaos to efficiently encrypt multiple images.Moreover,the keys are processed using an elliptic curve cryptosystem,eliminating the need for an additional channel to transmit the keys,thus enhancing security.Experimental results and algorithm analysis demonstrate that the algorithm offers strong security and high efficiency,making it suitable for remote sensing image encryption tasks.展开更多
[Objectives]To explore the relationship between vegetation index and forest surface fuel load.[Methods]UAV multispectral remote sensing was used to obtain large-scale forest images and obtain structural data of forest...[Objectives]To explore the relationship between vegetation index and forest surface fuel load.[Methods]UAV multispectral remote sensing was used to obtain large-scale forest images and obtain structural data of forest surface fuel load.This experimental area was located in Gaoming District,Foshan City,Guangdong Province.The average surface fuel load of the experimental area was as high as 39.33 t/ha,and the forest surface fuel load of Pinus elliottii was the highest.[Results]The normalized difference vegetation index(NDVI)and enhanced vegetation index(EVI)had a moderately strong correlation with the forest surface fuel load.The regression model of NDVI(X)and forest surface fuel load(Y)was established:Y=-5.9354X+8.4663,and the regression model of EVI(X)and forest surface fuel load(Y)was established:Y=-5.8485X+6.7271.The study also found that the linear relationship between NDVI and surface fuel load was more significant.[Conclusions]Both NDVI and EVI have moderately strong correlations with forest surface fuel load.NDVI is moderately or strongly correlated with the surface fuel load of Pinus massoniana forest,shrub grassland,broad-leaf forest and bamboo forest,while EVI is only strongly correlated with surface fuel load of broad-leaf forest and bamboo forest.It is expected that the relationship between other vegetation indices and forest surface fuel load can be obtained by the method in this study,so as to find a more universal vegetation index for calculating surface fuel load.展开更多
The sense of telepresence is known to be essential in teleoperation environments, where the operator is physically separated from the vehicle. Usually only a visual feedback is provided, but it has been shown that by ...The sense of telepresence is known to be essential in teleoperation environments, where the operator is physically separated from the vehicle. Usually only a visual feedback is provided, but it has been shown that by extending the visual interface with haptic feedback, that is complementing the visual information through the sense of touch, the teleoperator has a better perception of information from the remote environment and its constraints. This paper focuses on a novel concept of haptic cueing for an airborne obstacle avoidance task; the novel cueing algorithm was designed to appear ＂natural＂ to the operator, and to improve the human-machine interface without directly acting on the actual aircraft commands. Two different haptic aiding concepts for obstacle avoidance support are presented： an existing and widely used system, belonging to what we called the Direct Haptic Aid （DItA） approach class, and a novel one based on the Indirect Haptic Aid （IHA） approach class. Tests with human operators show that a net improvement in terms of performance （i.e., the number of collisions） is provided by employing the 1HA haptic cue as compared to both the DHA haptic cue and/or the visual cues only. The results clearly show that the IHA philosophy is a valid alternative to the other commonly used approaches, which fall in the DHA category.展开更多
Recently,unmanned aerial vehicle(UAV)-aided free-space optical(FSO)communication has attracted widespread attentions.However,most of the existing research focuses on communication performance only.The authors investig...Recently,unmanned aerial vehicle(UAV)-aided free-space optical(FSO)communication has attracted widespread attentions.However,most of the existing research focuses on communication performance only.The authors investigate the integrated scheduling of communication,sensing,and control for UAV-aided FSO communication systems.Initially,a sensing-control model is established via the control theory.Moreover,an FSO communication channel model is established by considering the effects of atmospheric loss,atmospheric turbulence,geometrical loss,and angle-of-arrival fluctuation.Then,the relationship between the motion control of the UAV and radial displacement is obtained to link the control aspect and communication aspect.Assuming that the base station has instantaneous channel state information(CSI)or statistical CSI,the thresholds of the sensing-control pattern activation are designed,respectively.Finally,an integrated scheduling scheme for performing communication,sensing,and control is proposed.Numerical results indicate that,compared with conventional time-triggered scheme,the proposed integrated scheduling scheme obtains comparable communication and control performance,but reduces the sensing consumed power by 52.46%.展开更多
Unmanned Aerial Vehicles(UAVs)have become indispensable for intelligent traffic monitoring,particularly in low-light conditions,where traditional surveillance systems struggle.This study presents a novel deep learning...Unmanned Aerial Vehicles(UAVs)have become indispensable for intelligent traffic monitoring,particularly in low-light conditions,where traditional surveillance systems struggle.This study presents a novel deep learning-based framework for nighttime aerial vehicle detection and classification that addresses critical challenges of poor illumination,noise,and occlusions.Our pipeline integrates MSRCR enhancement with OPTICS segmentation to overcome low-light challenges,while YOLOv10 enables accurate vehicle localization.The framework employs GLOH and Dense-SIFT for discriminative feature extraction,optimized using the Whale Optimization Algorithm to enhance classification performance.A Swin Transformer-based classifier provides the final categorization,leveraging hierarchical attention mechanisms for robust performance.Extensive experimentation validates our approach,achieving detection mAP@0.5 scores of 91.5%(UAVDT)and 89.7%(VisDrone),alongside classification accuracies of 95.50%and 92.67%,respectively.These results outperform state-of-the-art methods by up to 5.10%in accuracy and 4.2%in mAP,demonstrating the framework’s effectiveness for real-time aerial surveillance and intelligent traffic management in challenging nighttime environments.展开更多
基金Supported by Huizhou Municipal Stable Grain and Oil Production Award and Subsidy Project"2025 Single-spray Multi-Promotion Project of Silicon Fertilizer on Rice Crops Using UAVs of Huicheng District".
文摘[Objectives]To investigate the effects of silicon fertilizer spraying on the growth,yield,quality,and overall benefits of rice cultivation.[Methods]A systematic experiment involving the single-spray multi-promotion technology of silicon fertilizer via unmanned aerial vehicles(UAVs)was conducted in three representative rice-growing areas:Ma'an Town,Shuikou Subdistrict,and Luzhou Town.[Results]The spraying of silicon fertilizer markedly enhanced the root development of rice,resulting in increased tiller number,plant height,stem thickness,panicle length,and 1000-grain weight,thereby effectively improving both yield and quality.This treatment exerted six primary beneficial effects:promoting robust and stable seedling growth,enhancing stress resistance,reducing reliance on chemical fertilizers,improving quality,increasing economic benefits,and significantly advancing ecological and social benefits.[Conclusions]The application of silicon fertilizer through spraying is an effective agronomic practice that simultaneously promotes increased rice yield,improved quality,enhanced efficiency,and the sustainable development of resources and the environment.
基金supported through Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R508)Princess Nourah bint Abdulrahman University,Riyadh,Saudi ArabiaThe research team thanks the Deanship of Graduate Studies and Scientific Research at Najran University for supporting the research project through the Nama’a program,with the project code NU/GP/SERC/13/18-5.
文摘Unmanned Aerial Vehicles(UAVs)are increasingly employed in traffic surveillance,urban planning,and infrastructure monitoring due to their cost-effectiveness,flexibility,and high-resolution imaging.However,vehicle detection and classification in aerial imagery remain challenging due to scale variations from fluctuating UAV altitudes,frequent occlusions in dense traffic,and environmental noise,such as shadows and lighting inconsistencies.Traditional methods,including sliding-window searches and shallow learning techniques,struggle with computational inefficiency and robustness under dynamic conditions.To address these limitations,this study proposes a six-stage hierarchical framework integrating radiometric calibration,deep learning,and classical feature engineering.The workflow begins with radiometric calibration to normalize pixel intensities and mitigate sensor noise,followed by Conditional Random Field(CRF)segmentation to isolate vehicles.YOLOv9,equipped with a bi-directional feature pyramid network(BiFPN),ensures precise multi-scale object detection.Hybrid feature extraction employs Maximally Stable Extremal Regions(MSER)for stable contour detection,Binary Robust Independent Elementary Features(BRIEF)for texture encoding,and Affine-SIFT(ASIFT)for viewpoint invariance.Quadratic Discriminant Analysis(QDA)enhances feature discrimination,while a Probabilistic Neural Network(PNN)performs Bayesian probability-based classification.Tested on the Roundabout Aerial Imagery(15,474 images,985K instances)and AU-AIR(32,823 instances,7 classes)datasets,the model achieves state-of-the-art accuracy of 95.54%and 94.14%,respectively.Its superior performance in detecting small-scale vehicles and resolving occlusions highlights its potential for intelligent traffic systems.Future work will extend testing to nighttime and adverse weather conditions while optimizing real-time UAV inference.
基金This work was supported by the Ministry of Education,Youth and Sports of the Czech Republic within the National Programme for Sustainability I[grant number LO1415]partly by EEA Grants if Iceland,Liechtenstein and Norway[grant number EHP-CZ02-OV-1-019-2014].
文摘The study investigates the potential of UAV-based remote sensing technique for monitoring of Norway spruce health condition in the affected forest areas.The objectives are:(1)to test the applicability of UAV visible an near-infrared(VNIR)and geometrical data based on Z values of point dense cloud(PDC)raster to separate forest species and dead trees in the study area;(2)to explore the relationship between UAV VNIR data and individual spruce health indicators from field sampling;and(3)to explore the possibility of the qualitative classification of spruce health indicators.Analysis based on NDVI and PDC raster was successfully applied for separation of spruce and silver fir,and for identification of dead tree category.Separation between common beech and fir was distinguished by the object-oriented image analysis.NDVI was able to identify the presence of key indicators of spruce health,such as mechanical damage on stems and stem resin exudation linked to honey fungus infestation,while stem damage by peeling was identified at the significance margin.The results contributed to improving separation of coniferous(spruce and fir)tree species based on VNIR and PDC raster UAV data,and newly demonstrated the potential of NDVI for qualitative classification of spruce trees.The proposed methodology can be applicable for monitoring of spruce health condition in the local forest sites.
基金National Key Research and Development Program of China,No.2018YFB0505303Science and Technology Support Project of China,No.2014BAL01804Science and Technology Project of Sichuan Provincial Department of Education,No.11ZA098
文摘Investigating and monitoring the area of cultivated land reclaimed from rural settlements is important to optimize rural land use and understand spatial patterns. Measuring cultivated land area is costly and inefficient, however, as this land use type is often widely dispersed and scattered. A new method is therefore explored in this study that utilizes a Phantom2 Vision +(P2V), one kind of Dajiang(DJI) unmanned aerial vehicle(UAV). The method proposed here includes generating rural settlement images using a P2V UAV, subsequently correcting them using a camera lens model, matching them with geo-coded high resolution alternatives, mosaicking them, measuring the area of cultivated land reclaimed from rural settlements, evaluating measurement accuracy, and analyzing overall efficiency.The results of this study show that use of a P2V UAV is reasonable in price, less than 8000 yuan(RMB), and that this method is able to measure cultivated land area reclaimed from rural settlements with 99% accuracy. This method is therefore low cost, highly efficient, and low risk, as well as being easy to learn and use. This UAV-based approach is also likely to be easily popularized and be particularly useful both for application across plains and flats as well as over mountains and hills. The method proposed in this study is also likely to prove beneficial for monitoring and managing rural land use and future consolidation.
文摘This paper describes the general optimization design method of Solar-Powered Unmanned Aerial Vehicle which priority considering propulsion system planning. Based on the traditional solar powered aircraft design method, the propulsion system top-level target parameters which affect the path planning are integrated into the general optimization design. According to the typical mission requirements of Solar-Powered Unmanned Aerial Vehicle, considering the design variables such as wing area, aspect ratio, design mission date and so on, the general optimization is carried out with the minimum aircraft weight as the optimization objective. The influence of wing area and aspect ratio on the optimal design results is analyzed and compared with the traditional design method. The results show that the general design method of Solar-Powered Unmanned Aerial Vehicle for priority considering propulsion system can greatly reduce the electricity demand of energy storage battery, greatly reduce the aircraft weight of Solar-Powered Unmanned Aerial Vehicle.
基金Sanming Project of Medicine in Shenzhen(No.SZSM201911007)Shenzhen Stability Support Plan(20200824145152001)。
文摘BACKGROUND:Rapid on-site triage is critical after mass-casualty incidents(MCIs)and other mass injury events.Unmanned aerial vehicles(UAVs)have been used in MCIs to search and rescue wounded individuals,but they mainly depend on the UAV operator’s experience.We used UAVs and artificial intelligence(AI)to provide a new technique for the triage of MCIs and more efficient solutions for emergency rescue.METHODS:This was a preliminary experimental study.We developed an intelligent triage system based on two AI algorithms,namely OpenPose and YOLO.Volunteers were recruited to simulate the MCI scene and triage,combined with UAV and Fifth Generation(5G)Mobile Communication Technology real-time transmission technique,to achieve triage in the simulated MCI scene.RESULTS:Seven postures were designed and recognized to achieve brief but meaningful triage in MCIs.Eight volunteers participated in the MCI simulation scenario.The results of simulation scenarios showed that the proposed method was feasible in tasks of triage for MCIs.CONCLUSION:The proposed technique may provide an alternative technique for the triage of MCIs and is an innovative method in emergency rescue.
基金supported in part by the Strategic Research Council at the Academy of Finland project“Competence Based Growth Through Integrated Disruptive Technologies of 3D Digitalization,Robotics,Geospatial Information and Image Processing/Computing-Point Cloud Ecosystem(293389,314312),Academy of Finland projects“Estimating Forest Resources and Quality-related Attributes Using Automated Methods and Technologies”(334830,334829)”,“Monitoring and understanding forest ecosystem cycles”(334060)。
文摘Background:Current automated forest investigation is facing a dilemma over how to achieve high tree-and plotlevel completeness while maintaining a high cost and labor efficiency.This study tackles the challenge by exploring a new concept that enables an efficient fusion of aerial and terrestrial perspectives for digitizing and characterizing individual trees in forests through an Unmanned Aerial Vehicle(UAV)that flies above and under canopies in a single operation.The advantage of such concept is that the aerial perspective from the above-canopy UAV and the terrestrial perspective from the under-canopy UAV can be seamlessly integrated in one flight,thus grants the access to simultaneous high completeness,high efficiency,and low cost.Results:In the experiment,an approximately 0.5 ha forest was covered in ca.10 min from takeoff to landing.The GNSS-IMU based positioning supports a geometric accuracy of the produced point cloud that is equivalent to that of the mobile mapping systems,which leads to a 2–4 cm RMSE of the diameter at the breast height estimates,and a 4–7 cm RMSE of the stem curve estimates.Conclusions:Results of the experiment suggested that the integrated flight is capable of combining the high completeness of upper canopies from the above-canopy perspective and the high completeness of stems from the terrestrial perspective.Thus,it is a solution to combine the advantages of the terrestrial static,the mobile,and the above-canopy UAV observations,which is a promising step forward to achieve a fully autonomous in situ forest inventory.Future studies should be aimed to further improve the platform positioning,and to automatize the UAV operation.
基金Supported by the China Agriculture Research System(CARS-170303)the Special Fund for the Construction of Modern Agricultural Technology System in Yunnan Province+1 种基金the Training Project of Yunling Industry and Technology Leading Talents(2018LJRC56)the Project for the Cooperation between Scientific Research Institutes and Enterprises in Nanhua of Lincang(LT11-12E120810-002<12-13E130328-041)
文摘This research was aimed at the defects in traditional artificial spraying control method and the problems such as the difficulty in pesticides applying,labor shortage and low operating efficiency in the middle and late stage of sugarcane high stalk crops.The aerial pesticide application technology for sugarcane main diseases and pests was systematically developed and demonstrated from the aspects of aircraft type choice,selection of special pesticides and auxiliaries,integration of pesticides and equipment,field operation,technical specifications,and large-scale application organization mode.The UAV model and flight technical parameters suitable for the sugarcane planting area in low-latitude plateau were analyzed,and the optimal agent formulation combination and application technology of the UAV flight control were screened out,and the UAV flight control was applied to the major sugarcane pests and diseases control in the low-latitude plateau in large scale(UAV flight control was popularized and applied to 15 527 hm 2 in 2018).The research results provided mature whole-process technical support for the normalization of the application of the UVA flight control of major sugarcane pests and diseases.The UAV control technology for major sugarcane pests and diseases had the advantages of ultra-low pesticides applying dosage and high operating efficiency,and could effectively solve the problems such as the difficulty in pesticides applying,labor shortage and low operating efficiency in the middle late growth stage of high stalk crops.This technology successfully opened up a simple,efficient and new way for the effective control of major sugarcane pests and diseases,and practically accelerated the process of integrated control and prevention of sugarcane pests and diseases.In addition,this technology had an extremely significant effect on reducing the loss of sugarcane farmers and enterprises caused by the epidemic and outbreak of sugarcane pests and diseases,increasing sugarcane yield and sugar content.At the same time,this technology played an important role in realizing the whole-process precise control of sugarcane pests and diseases,improving the quality and increasing the efficiency of sugarcane,and guaranteeing the national sugar safety.
基金Supported by the Special Fund for the Cultivation of Outstanding Young Scientific and Technological Talents(2015-2018)~~
文摘Unmanned aerial vehicle technology was used to survey the vegetation coverage of typical urban-rural fringe, and descriptive statistics and geostatistical methods were used to analyze the urban-rural fringe of spatial heterogeneity of vegetation coverage. The results showed that vegetation coverage in the study area was 27.2176% with the coefficient of variation of 31.7786%; that the vegetation coverage in separation distance of 〈0.18＇ showed positive spatial correlation, and the spatial correlation of vegetation coverage in separation distance of 〈0.18＇ was greater than that in 〉0.18＇; that the best fitting model for Semivariance function was exponential model with spatial variation ratio 0.726, which showed strong spatial correlation, and the spatial correlated scale was 0.18＇; that the vegetation coverage data in the study area was relatively stable, and the instability mainly occurred on the border of the study area and the surroundings.
基金support from Natural Science Fund of China[32171789].
文摘Close-range sensing has yet to attain the status of being a dependable source for in situ forest information as the conventional field inventory.Each solution has its advantages and disadvantages in terms of accuracy,completeness,and efficiency.For a forest area,Terrestrial Laser Scanning(TLS)has the highest data quality,but is limited to static perspectives and lack the efficiency.Mobile Mapping Systems(MMS)systems gain on the efficiency but compromise the data quality.More recently,under-canopy UAV caught attentions for its potential to leverage the advantages of both TLS and MMS systems.This study demonstrates the feasibility of autonomous forest in situ investigation using an autonomous under-canopy UAV Laser Scanning(ULS)system,and evaluates the performance of such system in deriving key forest and tree attributes through a comparison with other close-range sensing systems such as the TLS and the Personal Laser Scanning(PLS).The under-canopy ULS system uses an onboard LiDAR sensor to aid its self-traverse in an unknown forest environment and to collect point cloud data during its movement inside the forest.Key factors influencing the systems’overall performance were investigated via various experiments.The point cloud data collected by the under canopy autonomous ULS system deliver similar stem capturing capacity as TLS in single layer forest stands with less undergrowth.The RMSEs of the DBH estimates were 0.81 cm(3.80%),4.12cm(19.92%),and 5.13cm(22.01%),respectively.The RMSEs of the stem curve estimates were 1.27 cm(5.48%),3.97 cm(17.63%),and 5.18 cm(22.49%),respectively.The geometric accuracy and the completeness of the point cloud significantly improved when the trajectory was densified.More studies on autonomous route planning in complex unknown forest is required to improve the system mobility,data quality,and the applicability of such systems in future practical forest in situ observations.
基金Project(201412016)supported by the Special Fund for Public Projects of National Administration of Surveying,Mapping and Geoinformation of ChinaProject(51174287)supported by the National Natural Science Foundation of China
文摘Numerous coal fires burn underneath the Datong coalfield because of indiscriminate mining.Landsat TM/ETM,unmanned aerial vehicle（UAV）,and infrared thermal imager were employed to monitor underground coal fires in the Majiliang mining area.The thermal field distributions of this area in 2000,2002,2006,2007,and 2009 were obtained using Landsat TM/ETM.The changes in the distribution were then analyzed to approximate the locations of the coal fires.Through UAV imagery employed at a very high resolution（0.2 m）,the texture information,linear features,and brightness of the ground fissures in the coal fire area were determined.All these data were combined to build a knowledge model of determining fissures and were used to support underground coal fire detection.An infrared thermal imager was used to map the thermal field distribution of areas where coal fire is serious.Results were analyzed to identify the hot spot trend and the depth of the burning point.
基金funded by the National Key Technologies R&D Program of China (Grants No. 2017YFC0505104)the Key Laboratory of Digital Mapping and Land Information Application of National Administration of Surveying, Mapping and Geoinformation of China (Grants No. DM2016SC09)
文摘At 5:39 am on June 24, 2017, a landslide occurred in the village of Xinmo in Maoxian County, Aba Tibet and Qiang Autonomous Prefecture(Sichuan Province, Southwest China). On June 25, aerial images were acquired from an unmanned aerial vehicle(UAV), and a digital elevation model(DEM) was processed. Landslide geometrical features were then analyzed. These are the front and rear edge elevation, accumulation area and horizontal sliding distance. Then, the volume and the spatial distribution of the thickness of the deposit were calculated from the difference between the DEM available before the landslide, and the UAV-derived DEM collected after the landslide. Also, the disaster was assessed using high-resolution satellite images acquired before the landslide. These include Quick Bird, Pleiades-1 and GF-2 images with spatial resolutions of 0.65 m, 0.70 m, and 0.80 m, respectively, and the aerial images acquired from the UAV after the landslide with a spatial resolution of 0.1 m. According to the analysis, the area of the landslide was 1.62 km2, and the volume of the landslide was 7.70 ± 1.46 million m3. The average thickness of the landslide accumulation was approximately 8 m. The landslide destroyed a total of 103 buildings. The area of destroyed farmlands was 2.53 ha, and the orchard area was reduced by 28.67 ha. A 2-km section of Songpinggou River was blocked and a 2.1-km section of township road No. 104 was buried. Constrained by the terrain conditions, densely populated and more economically developed areas in the upper reaches of the Minjiang River basin are mainly located in the bottom of the valleys. This is a dangerous area regarding landslide, debris flow and flash flood events Therefore, in mountainous, high-risk disaster areas, it is important to carefully select residential sites to avoid a large number of casualties.
基金Supported by the Fundamental Research Projects of Science&Technology Innovation and Development Plan in Yantai City(No.2022JCYJ041)the Natural Science Foundation of Shandong Province(Nos.ZR2022MD042,ZR2022MD028)+1 种基金the Seed Project of Yantai Institute of Coastal Zone Research,Chinese Academy of Sciences(No.YICE351030601)the NSFC Fund Project(No.42206240)。
文摘Since 2007,the Yellow Sea green tide has broken out every summer,causing great harm to the environment and society.Although satellite remote sensing(RS)has been used in biomass research,there are several shortcomings,such as mixed pixels,atmospheric interference,and difficult field validation.The biomass of green tide has been lacking a high-precision estimation method.In this study,high-resolution unmanned aerial vehicle(UAV)RS was used to quantitatively map the biomass of green tides.By utilizing experimental data from previous studies,a robust relationship was established to link biomass to the red-green-blue floating algae index(RGB-FAI).Then,the lab-based model for green tide biomass from visible images taken by the UAV camera was developed and validated by field measurements.Re sults show that the accurate and cost-effective method is able to estimate the green tide biomass and its changes in given local waters of the near and far seas.The study provided an effective complement to the traditional satellite RS,as well as high-precision quantitative techniques for decision-making in disaster management.
基金The authors would like to thank the Taif University for funding this work through Taif University Research Supporting,Project Number.(TURSP-2020/277),Taif University,Taif,Saudi Arabia.
文摘Latest advancements in the integration of camera sensors paves a way for newUnmannedAerialVehicles(UAVs)applications such as analyzing geographical(spatial)variations of earth science in mitigating harmful environmental impacts and climate change.UAVs have achieved significant attention as a remote sensing environment,which captures high-resolution images from different scenes such as land,forest fire,flooding threats,road collision,landslides,and so on to enhance data analysis and decision making.Dynamic scene classification has attracted much attention in the examination of earth data captured by UAVs.This paper proposes a new multi-modal fusion based earth data classification(MMF-EDC)model.The MMF-EDC technique aims to identify the patterns that exist in the earth data and classifies them into appropriate class labels.The MMF-EDC technique involves a fusion of histogram of gradients(HOG),local binary patterns(LBP),and residual network(ResNet)models.This fusion process integrates many feature vectors and an entropy based fusion process is carried out to enhance the classification performance.In addition,the quantum artificial flora optimization(QAFO)algorithm is applied as a hyperparameter optimization technique.The AFO algorithm is inspired by the reproduction and the migration of flora helps to decide the optimal parameters of the ResNet model namely learning rate,number of hidden layers,and their number of neurons.Besides,Variational Autoencoder(VAE)based classification model is applied to assign appropriate class labels for a useful set of feature vectors.The proposedMMF-EDCmodel has been tested using UCM and WHU-RS datasets.The proposed MMFEDC model attains exhibits promising classification results on the applied remote sensing images with the accuracy of 0.989 and 0.994 on the test UCM and WHU-RS dataset respectively.
基金supported by the National Natural Science Foundation of China(Grant No.91948303)。
文摘Remote sensing images carry crucial ground information,often involving the spatial distribution and spatiotemporal changes of surface elements.To safeguard this sensitive data,image encryption technology is essential.In this paper,a novel Fibonacci sine exponential map is designed,the hyperchaotic performance of which is particularly suitable for image encryption algorithms.An encryption algorithm tailored for handling the multi-band attributes of remote sensing images is proposed.The algorithm combines a three-dimensional synchronized scrambled diffusion operation with chaos to efficiently encrypt multiple images.Moreover,the keys are processed using an elliptic curve cryptosystem,eliminating the need for an additional channel to transmit the keys,thus enhancing security.Experimental results and algorithm analysis demonstrate that the algorithm offers strong security and high efficiency,making it suitable for remote sensing image encryption tasks.
基金Forestry Science and Technology Innovation Project of Guangdong Province(2018KJCX003).
文摘[Objectives]To explore the relationship between vegetation index and forest surface fuel load.[Methods]UAV multispectral remote sensing was used to obtain large-scale forest images and obtain structural data of forest surface fuel load.This experimental area was located in Gaoming District,Foshan City,Guangdong Province.The average surface fuel load of the experimental area was as high as 39.33 t/ha,and the forest surface fuel load of Pinus elliottii was the highest.[Results]The normalized difference vegetation index(NDVI)and enhanced vegetation index(EVI)had a moderately strong correlation with the forest surface fuel load.The regression model of NDVI(X)and forest surface fuel load(Y)was established:Y=-5.9354X+8.4663,and the regression model of EVI(X)and forest surface fuel load(Y)was established:Y=-5.8485X+6.7271.The study also found that the linear relationship between NDVI and surface fuel load was more significant.[Conclusions]Both NDVI and EVI have moderately strong correlations with forest surface fuel load.NDVI is moderately or strongly correlated with the surface fuel load of Pinus massoniana forest,shrub grassland,broad-leaf forest and bamboo forest,while EVI is only strongly correlated with surface fuel load of broad-leaf forest and bamboo forest.It is expected that the relationship between other vegetation indices and forest surface fuel load can be obtained by the method in this study,so as to find a more universal vegetation index for calculating surface fuel load.
文摘The sense of telepresence is known to be essential in teleoperation environments, where the operator is physically separated from the vehicle. Usually only a visual feedback is provided, but it has been shown that by extending the visual interface with haptic feedback, that is complementing the visual information through the sense of touch, the teleoperator has a better perception of information from the remote environment and its constraints. This paper focuses on a novel concept of haptic cueing for an airborne obstacle avoidance task; the novel cueing algorithm was designed to appear ＂natural＂ to the operator, and to improve the human-machine interface without directly acting on the actual aircraft commands. Two different haptic aiding concepts for obstacle avoidance support are presented： an existing and widely used system, belonging to what we called the Direct Haptic Aid （DItA） approach class, and a novel one based on the Indirect Haptic Aid （IHA） approach class. Tests with human operators show that a net improvement in terms of performance （i.e., the number of collisions） is provided by employing the 1HA haptic cue as compared to both the DHA haptic cue and/or the visual cues only. The results clearly show that the IHA philosophy is a valid alternative to the other commonly used approaches, which fall in the DHA category.
文摘Recently,unmanned aerial vehicle(UAV)-aided free-space optical(FSO)communication has attracted widespread attentions.However,most of the existing research focuses on communication performance only.The authors investigate the integrated scheduling of communication,sensing,and control for UAV-aided FSO communication systems.Initially,a sensing-control model is established via the control theory.Moreover,an FSO communication channel model is established by considering the effects of atmospheric loss,atmospheric turbulence,geometrical loss,and angle-of-arrival fluctuation.Then,the relationship between the motion control of the UAV and radial displacement is obtained to link the control aspect and communication aspect.Assuming that the base station has instantaneous channel state information(CSI)or statistical CSI,the thresholds of the sensing-control pattern activation are designed,respectively.Finally,an integrated scheduling scheme for performing communication,sensing,and control is proposed.Numerical results indicate that,compared with conventional time-triggered scheme,the proposed integrated scheduling scheme obtains comparable communication and control performance,but reduces the sensing consumed power by 52.46%.
基金supported through Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R508)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia。
文摘Unmanned Aerial Vehicles(UAVs)have become indispensable for intelligent traffic monitoring,particularly in low-light conditions,where traditional surveillance systems struggle.This study presents a novel deep learning-based framework for nighttime aerial vehicle detection and classification that addresses critical challenges of poor illumination,noise,and occlusions.Our pipeline integrates MSRCR enhancement with OPTICS segmentation to overcome low-light challenges,while YOLOv10 enables accurate vehicle localization.The framework employs GLOH and Dense-SIFT for discriminative feature extraction,optimized using the Whale Optimization Algorithm to enhance classification performance.A Swin Transformer-based classifier provides the final categorization,leveraging hierarchical attention mechanisms for robust performance.Extensive experimentation validates our approach,achieving detection mAP@0.5 scores of 91.5%(UAVDT)and 89.7%(VisDrone),alongside classification accuracies of 95.50%and 92.67%,respectively.These results outperform state-of-the-art methods by up to 5.10%in accuracy and 4.2%in mAP,demonstrating the framework’s effectiveness for real-time aerial surveillance and intelligent traffic management in challenging nighttime environments.
