Incremental image compression techniques using priori information are of significance to deal with the explosively increasing remote-sensing image data. However, the potential benefi ts of priori information are still...Incremental image compression techniques using priori information are of significance to deal with the explosively increasing remote-sensing image data. However, the potential benefi ts of priori information are still to be evaluated quantitatively for effi cient compression scheme designing. In this paper, we present a k-nearest neighbor(k-NN) based bypass image entropy estimation scheme, together with the corresponding mutual information estimation method. Firstly, we apply the k-NN entropy estimation theory to split image blocks, describing block-wise intra-frame spatial correlation while avoiding the curse of dimensionality. Secondly, we propose the corresponding mutual information estimator based on feature-based image calibration and straight-forward correlation enhancement. The estimator is designed to evaluate the compression performance gain of using priori information. Numerical results on natural and remote-sensing images show that the proposed scheme obtains an estimation accuracy gain by 10% compared with conventional image entropy estimators. Furthermore, experimental results demonstrate both the effectiveness of the proposed mutual information evaluation scheme, and the quantitative incremental compressibility by using the priori remote-sensing frames.展开更多
In this paper,we build a remote-sensing satellite imagery priori-information data set,and propose an approach to evaluate the robustness of remote-sensing image feature detectors.The building TH Priori-Information(TPI...In this paper,we build a remote-sensing satellite imagery priori-information data set,and propose an approach to evaluate the robustness of remote-sensing image feature detectors.The building TH Priori-Information(TPI)data set with 2297 remote sensing images serves as a standardized high-resolution data set for studies related to remote-sensing image features.The TPI contains 1)raw and calibrated remote-sensing images with high spatial and temporal resolutions(up to 2 m and 7 days,respectively),and 2)a built-in 3-D target area model that supports view position,view angle,lighting,shadowing,and other transformations.Based on TPI,we further present a quantized approach,including the feature recurrence rate,the feature match score,and the weighted feature robustness score,to evaluate the robustness of remote-sensing image feature detectors.The quantized approach gives general and objective assessments of the robustness of feature detectors under complex remote-sensing circumstances.Three remote-sensing image feature detectors,including scale-invariant feature transform(SIFT),speeded up robust features(SURF),and priori information based robust features(PIRF),are evaluated using the proposed approach on the TPI data set.Experimental results show that the robustness of PIRF outperforms others by over 6.2%.展开更多
The profiling method is the first method to select in measuring the remote-sensing reflectance. In the light of the characteristics of China’s coastal waters, we develop a new method to compensate the environment’s ...The profiling method is the first method to select in measuring the remote-sensing reflectance. In the light of the characteristics of China’s coastal waters, we develop a new method to compensate the environment’s effects with the downwelling (λ)rsi r radiance’s profile and to estimate the underwater remote-sensing reflectance r. (λ)rsT he result indicates that the relative deviation of repetitious r in one station is around 10 %.展开更多
Shadow detection is a crucial task in high-resolution remote-sensing image processing. Various shadow detection methods have been explored during the last decades. These methods did improve the detection accuracy but ...Shadow detection is a crucial task in high-resolution remote-sensing image processing. Various shadow detection methods have been explored during the last decades. These methods did improve the detection accuracy but are still not robust enough to get satisfactory results for failing to extract enough information from the original images. To take full advantage of various features of shadows, a new method combining edges information with the spectral and spatial information is proposed in this paper. As known, edge is one of the most important characteristics in the high-resolution remote-sensing images. Unfortunately, in shadow detection, it is a high-risk strategy to determine whether a pixel is the edge or not strictly because intensity values on shadow boundaries are always between those in shadow and non-shadow areas. Therefore, a soft edge description model is developed to describe the degree of each pixel belonging to the edges or not. Sequentially, the soft edge description is incorporating to a fuzzy clustering procedure based on HMRF (Hidden Markov Random Fields), in which more appropriate spatial contextual information can be used. More concretely, it consists of two components: the soft edge description model and an iterative shadow detection algorithm. Experiments on several remote sensing images have shown that the proposed method can obtain more accurate shadow detection results.展开更多
Due to the limitations of spatial bandwidth product and data transmission bandwidth,the field of view,resolution,and imaging speed constrain each other in an optical imaging system.Here,a fast-zoom and high-resolution...Due to the limitations of spatial bandwidth product and data transmission bandwidth,the field of view,resolution,and imaging speed constrain each other in an optical imaging system.Here,a fast-zoom and high-resolution sparse compound-eye camera(CEC)based on dual-end collaborative optimization is proposed,which provides a cost-effective way to break through the trade-off among the field of view,resolution,and imaging speed.In the optical end,a sparse CEC based on liquid lenses is designed,which can realize large-field-of-view imaging in real time,and fast zooming within 5 ms.In the computational end,a disturbed degradation model driven super-resolution network(DDMDSR-Net)is proposed to deal with complex image degradation issues in actual imaging situations,achieving high-robustness and high-fidelity resolution enhancement.Based on the proposed dual-end collaborative optimization framework,the angular resolution of the CEC can be enhanced from 71.6"to 26.0",which provides a solution to realize high-resolution imaging for array camera dispensing with high optical hardware complexity and data transmission bandwidth.Experiments verify the advantages of the CEC based on dual-end collaborative optimization in high-fidelity reconstruction of real scene images,kilometer-level long-distance detection,and dynamic imaging and precise recognition of targets of interest.展开更多
This article expounds the vital role undertaken by telemetry in monitoring and assessment in flood control in China. By analyzing the data from their surveys, the authors give an all-round exposition of problems in th...This article expounds the vital role undertaken by telemetry in monitoring and assessment in flood control in China. By analyzing the data from their surveys, the authors give an all-round exposition of problems in the country’s drive for disaster reduction and propose countermeasures in the fight against the possible flood problems in the future.展开更多
Observatories typically deploy all-sky cameras for monitoring cloud cover and weather conditions.However,many of these cameras lack scientific-grade sensors,r.esulting in limited photometric precision,which makes calc...Observatories typically deploy all-sky cameras for monitoring cloud cover and weather conditions.However,many of these cameras lack scientific-grade sensors,r.esulting in limited photometric precision,which makes calculating the sky area visibility distribution via extinction measurement challenging.To address this issue,we propose the Photometry-Free Sky Area Visibility Estimation(PFSAVE)method.This method uses the standard magnitude of the faintest star observed within a given sky area to estimate visibility.By employing a pertransformation refitting optimization strategy,we achieve a high-precision coordinate transformation model with an accuracy of 0.42 pixels.Using the results of HEALPix segmentation is also introduced to achieve high spatial resolution.Comprehensive analysis based on real allsky images demonstrates that our method exhibits higher accuracy than the extinction-based method.Our method supports both manual and robotic dynamic scheduling,especially under partially cloudy conditions.展开更多
This paper presents a high-speed and robust dual-band infrared thermal camera based on an ARM CPU.The system consists of a low-resolution long-wavelength infrared detector,a digital temperature and humid⁃ity sensor,an...This paper presents a high-speed and robust dual-band infrared thermal camera based on an ARM CPU.The system consists of a low-resolution long-wavelength infrared detector,a digital temperature and humid⁃ity sensor,and a CMOS sensor.In view of the significant contrast between face and background in thermal infra⁃red images,this paper explores a suitable accuracy-latency tradeoff for thermal face detection and proposes a tiny,lightweight detector named YOLO-Fastest-IR.Four YOLO-Fastest-IR models(IR0 to IR3)with different scales are designed based on YOLO-Fastest.To train and evaluate these lightweight models,a multi-user low-resolution thermal face database(RGBT-MLTF)was collected,and the four networks were trained.Experiments demon⁃strate that the lightweight convolutional neural network performs well in thermal infrared face detection tasks.The proposed algorithm outperforms existing face detection methods in both positioning accuracy and speed,making it more suitable for deployment on mobile platforms or embedded devices.After obtaining the region of interest(ROI)in the infrared(IR)image,the RGB camera is guided by the thermal infrared face detection results to achieve fine positioning of the RGB face.Experimental results show that YOLO-Fastest-IR achieves a frame rate of 92.9 FPS on a Raspberry Pi 4B and successfully detects 97.4%of faces in the RGBT-MLTF test set.Ultimate⁃ly,an infrared temperature measurement system with low cost,strong robustness,and high real-time perfor⁃mance was integrated,achieving a temperature measurement accuracy of 0.3℃.展开更多
This paper designs and implements a single-camera 360°panoramic imaging system based on motor-driven fisheye rotation.The system utilizes a stepper motor for precise angular control,enabling the camera to rotate ...This paper designs and implements a single-camera 360°panoramic imaging system based on motor-driven fisheye rotation.The system utilizes a stepper motor for precise angular control,enabling the camera to rotate around its optical center to capture multi-view images,thereby avoiding the parallax and geometric mismatch problems inherent in traditional multi-camera configurations.To address the strong distortion characteristics of fisheye images,an equidistant projection model is adopted for distortion correction.On this basis,a brightness normalization method combining global linear brightness correction and local illumination compensation is proposed to enhance stitching consistency.By establishing a geometry model constrained by camera rotation and integrating cylindrical projection with cosine-weighted blending,the system achieves high-precision panoramic stitching and seamless visual transitions.展开更多
Photomechanics is a crucial branch of solid mechanics.The localization of point targets constitutes a fundamental problem in optical experimental mechanics,with extensive applications in various missions of unmanned a...Photomechanics is a crucial branch of solid mechanics.The localization of point targets constitutes a fundamental problem in optical experimental mechanics,with extensive applications in various missions of unmanned aerial vehicles.Localizing moving targets is crucial for analyzing their motion characteristics and dynamic properties.Reconstructing the trajectories of points from asynchronous cameras is a significant challenge.It encompasses two coupled sub-problems:Trajectory reconstruction and camera synchronization.Present methods typically address only one of these sub-problems individually.This paper proposes a 3D trajectory reconstruction method for point targets based on asynchronous cameras,simultaneously solving both sub-problems.Firstly,we extend the trajectory intersection method to asynchronous cameras to resolve the limitation of traditional triangulation that requires camera synchronization.Secondly,we develop models for camera temporal information and target motion,based on imaging mechanisms and target dynamics characteristics.The parameters are optimized simultaneously to achieve trajectory reconstruction without accurate time parameters.Thirdly,we optimize the camera rotations alongside the camera time information and target motion parameters,using tighter and more continuous constraints on moving points.The reconstruction accuracy is significantly improved,especially when the camera rotations are inaccurate.Finally,the simulated and real-world experimental results demonstrate the feasibility and accuracy of the proposed method.The real-world results indicate that the proposed algorithm achieved a localization error of 112.95 m at an observation distance range of 15-20 km.展开更多
It is important to understand the development of joints and fractures in rock masses to ensure drilling stability and blasting effectiveness.Traditional manual observation techniques for identifying and extracting fra...It is important to understand the development of joints and fractures in rock masses to ensure drilling stability and blasting effectiveness.Traditional manual observation techniques for identifying and extracting fracture characteristics have been proven to be inefficient and prone to subjective interpretation.Moreover,conventional image processing algorithms and classical deep learning models often encounter difficulties in accurately identifying fracture areas,resulting in unclear contours.This study proposes an intelligent method for detecting internal fractures in mine rock masses to address these challenges.The proposed approach captures a nodal fracture map within the targeted blast area and integrates channel and spatial attention mechanisms into the ResUnet(RU)model.The channel attention mechanism dynamically recalibrates the importance of each feature channel,and the spatial attention mechanism enhances feature representation in key areas while minimizing background noise,thus improving segmentation accuracy.A dynamic serpentine convolution module is also introduced that adaptively adjusts the shape and orientation of the convolution kernel based on the local structure of the input feature map.Furthermore,this method enables the automatic extraction and quantification of borehole nodal fracture information by fitting sinusoidal curves to the boundaries of the fracture contours using the least squares method.In comparison to other advanced deep learning models,our enhanced RU demonstrates superior performance across evaluation metrics,including accuracy,pixel accuracy(PA),and intersection over union(IoU).Unlike traditional manual extraction methods,our intelligent detection approach provides considerable time and cost savings,with an average error rate of approximately 4%.This approach has the potential to greatly improve the efficiency of geological surveys of borehole fractures.展开更多
This study presents a drone-based aerial imaging method for automated rice seedling detection and counting in paddy fields.Utilizing a drone equipped with a high-resolution camera,images are captured 14 days postsowin...This study presents a drone-based aerial imaging method for automated rice seedling detection and counting in paddy fields.Utilizing a drone equipped with a high-resolution camera,images are captured 14 days postsowing at a consistent altitude of six meters,employing autonomous flight for uniform data acquisition.The approach effectively addresses the distinct growth patterns of both single and clustered rice seedlings at this early stage.The methodology follows a two-step process:first,the GoogleNet deep learning network identifies the location and center points of rice plants.Then,the U-Net deep learning network performs classification and counting of individual plants and clusters.This combination of deep learning models achieved a 90%accuracy rate in classifying and counting both single and clustered seedlings.To validate the method’s effectiveness,results were compared against traditional manual counting conducted by agricultural experts.The comparison revealed minimal discrepancies,with a variance of only 2–4 clumps per square meter,confirming the reliability of the proposed method.This automated approach offers significant benefits by providing an efficient,accurate,and scalable solution for monitoring seedling growth.It enables farmers to optimize fertilizer and pesticide application,improve resource allocation,and enhance overall crop management,ultimately contributing to increased agricultural productivity.展开更多
Closed thoracic drainage can be performed using a steel-needle-guided chest tube to treat pleural effusion or pneumothorax in clinics.However,the puncture procedure during surgery is invisible,increasing the risk of s...Closed thoracic drainage can be performed using a steel-needle-guided chest tube to treat pleural effusion or pneumothorax in clinics.However,the puncture procedure during surgery is invisible,increasing the risk of surgical failure.Therefore,it is necessary to design a visualization system for closed thoracic drainage.Augmented reality(AR)technology can assist in visualizing the internal anatomical structure and determining the insertion point on the body surface.The structure of the currently used steel-needle-guided chest tube was modified by integrating it with an ultrafine diameter camera to provide real-time visualization of the puncture process.After simulation experiments,the overall registration error of the AR method was measured to be within(3.59±0.53)mm,indicating its potential for clinical application.The ultrafine diameter camera module and improved steel-needle-guided chest tube can timely reflect the position of the needle tip in the human body.A comparative experiment showed that video guidance could improve the safety of the puncture process compared to the traditional method.Finally,a qualitative evaluation of the usability of the system was conducted through a questionnaire.This system facilitates the visualization of closed thoracic drainage puncture procedure and pro-vides an implementation scheme to enhance the accuracy and safety of the operative step,which is conducive to reducing the learning curve and improving the proficiency of the doctors.展开更多
The estimation of orientation parameters and correction of lens distortion are crucial problems in the field of Unmanned Aerial Vehicles(UAVs)photogrammetry.In recent years,the utilization of UAVs for aerial photogram...The estimation of orientation parameters and correction of lens distortion are crucial problems in the field of Unmanned Aerial Vehicles(UAVs)photogrammetry.In recent years,the utilization of UAVs for aerial photogrammetry has witnessed a surge in popularity.Typically,UAVs are equipped with low-cost non-metric cameras and a Position and Orientation System(POS).Unfortunately,the Interior Orientation Parameters(IOPs)of the non-metric cameras are not fixed.Whether the lens distortions are large or small,they effect the image coordinates accordingly.Additionally,Inertial Measurement Units(IMUs)often have observation errors.To address these challenges and improve parameter estimation for UAVs Light Detection and Ranging(LiDAR)and photogrammetry,this paper analyzes the accuracy of POS observations obtained from Global Navigation Satellite System Real Time Kinematic(GNSS-RTK)and IMU data.A method that incorporates additional known conditions for parameter estimation,a series of algorithms to simultaneously solve for IOPs,Exterior Orientation Parameters(EOPs),and camera lens distortion correction parameters are proposed.Extensive experiments demonstrate that the coordinates measured by GNSS-RTK can be directly used as linear EOPs;however,angular EOP measurements from IMUs exhibit relatively large errors compared to adjustment results and require correction during the adjustment process.The IOPs of non-metric cameras vary slightly between images but need to be treated as unknown parameters in high precision applications.Furthermore,it is found that the Ebner systematic error model is sensitive to the choice of the magnification parameter of the photographic baseline length in images,it should be set as less than or equal to one third of the photographic baseline to ensure stable solutions.展开更多
基金supported by National Basic Research Project of China(2013CB329006)National Natural Science Foundation of China(No.61622110,No.61471220,No.91538107)
文摘Incremental image compression techniques using priori information are of significance to deal with the explosively increasing remote-sensing image data. However, the potential benefi ts of priori information are still to be evaluated quantitatively for effi cient compression scheme designing. In this paper, we present a k-nearest neighbor(k-NN) based bypass image entropy estimation scheme, together with the corresponding mutual information estimation method. Firstly, we apply the k-NN entropy estimation theory to split image blocks, describing block-wise intra-frame spatial correlation while avoiding the curse of dimensionality. Secondly, we propose the corresponding mutual information estimator based on feature-based image calibration and straight-forward correlation enhancement. The estimator is designed to evaluate the compression performance gain of using priori information. Numerical results on natural and remote-sensing images show that the proposed scheme obtains an estimation accuracy gain by 10% compared with conventional image entropy estimators. Furthermore, experimental results demonstrate both the effectiveness of the proposed mutual information evaluation scheme, and the quantitative incremental compressibility by using the priori remote-sensing frames.
基金the National Key Research and Development Program of China under Grant 2018YFF0301205in part by the National Natural Science Foundation of China under Grant NSFC 61925105 and Grant 61801260.
文摘In this paper,we build a remote-sensing satellite imagery priori-information data set,and propose an approach to evaluate the robustness of remote-sensing image feature detectors.The building TH Priori-Information(TPI)data set with 2297 remote sensing images serves as a standardized high-resolution data set for studies related to remote-sensing image features.The TPI contains 1)raw and calibrated remote-sensing images with high spatial and temporal resolutions(up to 2 m and 7 days,respectively),and 2)a built-in 3-D target area model that supports view position,view angle,lighting,shadowing,and other transformations.Based on TPI,we further present a quantized approach,including the feature recurrence rate,the feature match score,and the weighted feature robustness score,to evaluate the robustness of remote-sensing image feature detectors.The quantized approach gives general and objective assessments of the robustness of feature detectors under complex remote-sensing circumstances.Three remote-sensing image feature detectors,including scale-invariant feature transform(SIFT),speeded up robust features(SURF),and priori information based robust features(PIRF),are evaluated using the proposed approach on the TPI data set.Experimental results show that the robustness of PIRF outperforms others by over 6.2%.
文摘The profiling method is the first method to select in measuring the remote-sensing reflectance. In the light of the characteristics of China’s coastal waters, we develop a new method to compensate the environment’s effects with the downwelling (λ)rsi r radiance’s profile and to estimate the underwater remote-sensing reflectance r. (λ)rsT he result indicates that the relative deviation of repetitious r in one station is around 10 %.
文摘Shadow detection is a crucial task in high-resolution remote-sensing image processing. Various shadow detection methods have been explored during the last decades. These methods did improve the detection accuracy but are still not robust enough to get satisfactory results for failing to extract enough information from the original images. To take full advantage of various features of shadows, a new method combining edges information with the spectral and spatial information is proposed in this paper. As known, edge is one of the most important characteristics in the high-resolution remote-sensing images. Unfortunately, in shadow detection, it is a high-risk strategy to determine whether a pixel is the edge or not strictly because intensity values on shadow boundaries are always between those in shadow and non-shadow areas. Therefore, a soft edge description model is developed to describe the degree of each pixel belonging to the edges or not. Sequentially, the soft edge description is incorporating to a fuzzy clustering procedure based on HMRF (Hidden Markov Random Fields), in which more appropriate spatial contextual information can be used. More concretely, it consists of two components: the soft edge description model and an iterative shadow detection algorithm. Experiments on several remote sensing images have shown that the proposed method can obtain more accurate shadow detection results.
基金financial supports from National Natural Science Foundation of China(Grant Nos.U23A20368 and 62175006)Academic Excellence Foundation of BUAA for PhD Students.
文摘Due to the limitations of spatial bandwidth product and data transmission bandwidth,the field of view,resolution,and imaging speed constrain each other in an optical imaging system.Here,a fast-zoom and high-resolution sparse compound-eye camera(CEC)based on dual-end collaborative optimization is proposed,which provides a cost-effective way to break through the trade-off among the field of view,resolution,and imaging speed.In the optical end,a sparse CEC based on liquid lenses is designed,which can realize large-field-of-view imaging in real time,and fast zooming within 5 ms.In the computational end,a disturbed degradation model driven super-resolution network(DDMDSR-Net)is proposed to deal with complex image degradation issues in actual imaging situations,achieving high-robustness and high-fidelity resolution enhancement.Based on the proposed dual-end collaborative optimization framework,the angular resolution of the CEC can be enhanced from 71.6"to 26.0",which provides a solution to realize high-resolution imaging for array camera dispensing with high optical hardware complexity and data transmission bandwidth.Experiments verify the advantages of the CEC based on dual-end collaborative optimization in high-fidelity reconstruction of real scene images,kilometer-level long-distance detection,and dynamic imaging and precise recognition of targets of interest.
文摘This article expounds the vital role undertaken by telemetry in monitoring and assessment in flood control in China. By analyzing the data from their surveys, the authors give an all-round exposition of problems in the country’s drive for disaster reduction and propose countermeasures in the fight against the possible flood problems in the future.
基金supported by Natural Science Foundation of Jilin Province(20210101468JC)Chinese Academy of Sciences and Local Government Cooperation Project(2023SYHZ0027,23SH04)National Natural Science Foundation of China(12273063&12203078)。
文摘Observatories typically deploy all-sky cameras for monitoring cloud cover and weather conditions.However,many of these cameras lack scientific-grade sensors,r.esulting in limited photometric precision,which makes calculating the sky area visibility distribution via extinction measurement challenging.To address this issue,we propose the Photometry-Free Sky Area Visibility Estimation(PFSAVE)method.This method uses the standard magnitude of the faintest star observed within a given sky area to estimate visibility.By employing a pertransformation refitting optimization strategy,we achieve a high-precision coordinate transformation model with an accuracy of 0.42 pixels.Using the results of HEALPix segmentation is also introduced to achieve high spatial resolution.Comprehensive analysis based on real allsky images demonstrates that our method exhibits higher accuracy than the extinction-based method.Our method supports both manual and robotic dynamic scheduling,especially under partially cloudy conditions.
基金Supported by the Fundamental Research Funds for the Central Universities(2024300443)the Natural Science Foundation of Jiangsu Province(BK20241224).
文摘This paper presents a high-speed and robust dual-band infrared thermal camera based on an ARM CPU.The system consists of a low-resolution long-wavelength infrared detector,a digital temperature and humid⁃ity sensor,and a CMOS sensor.In view of the significant contrast between face and background in thermal infra⁃red images,this paper explores a suitable accuracy-latency tradeoff for thermal face detection and proposes a tiny,lightweight detector named YOLO-Fastest-IR.Four YOLO-Fastest-IR models(IR0 to IR3)with different scales are designed based on YOLO-Fastest.To train and evaluate these lightweight models,a multi-user low-resolution thermal face database(RGBT-MLTF)was collected,and the four networks were trained.Experiments demon⁃strate that the lightweight convolutional neural network performs well in thermal infrared face detection tasks.The proposed algorithm outperforms existing face detection methods in both positioning accuracy and speed,making it more suitable for deployment on mobile platforms or embedded devices.After obtaining the region of interest(ROI)in the infrared(IR)image,the RGB camera is guided by the thermal infrared face detection results to achieve fine positioning of the RGB face.Experimental results show that YOLO-Fastest-IR achieves a frame rate of 92.9 FPS on a Raspberry Pi 4B and successfully detects 97.4%of faces in the RGBT-MLTF test set.Ultimate⁃ly,an infrared temperature measurement system with low cost,strong robustness,and high real-time perfor⁃mance was integrated,achieving a temperature measurement accuracy of 0.3℃.
基金Graduate Innovation Ability Training Program of the Hebei Provincial Department of Education,2025(Project No.:CXZZSS2025095)。
文摘This paper designs and implements a single-camera 360°panoramic imaging system based on motor-driven fisheye rotation.The system utilizes a stepper motor for precise angular control,enabling the camera to rotate around its optical center to capture multi-view images,thereby avoiding the parallax and geometric mismatch problems inherent in traditional multi-camera configurations.To address the strong distortion characteristics of fisheye images,an equidistant projection model is adopted for distortion correction.On this basis,a brightness normalization method combining global linear brightness correction and local illumination compensation is proposed to enhance stitching consistency.By establishing a geometry model constrained by camera rotation and integrating cylindrical projection with cosine-weighted blending,the system achieves high-precision panoramic stitching and seamless visual transitions.
基金supported by the Hunan Provin〓〓cial Natural Science Foundation for Excellent Young Scholars(Grant No.2023JJ20045)the National Natural Science Foundation of China(Grant No.12372189)。
文摘Photomechanics is a crucial branch of solid mechanics.The localization of point targets constitutes a fundamental problem in optical experimental mechanics,with extensive applications in various missions of unmanned aerial vehicles.Localizing moving targets is crucial for analyzing their motion characteristics and dynamic properties.Reconstructing the trajectories of points from asynchronous cameras is a significant challenge.It encompasses two coupled sub-problems:Trajectory reconstruction and camera synchronization.Present methods typically address only one of these sub-problems individually.This paper proposes a 3D trajectory reconstruction method for point targets based on asynchronous cameras,simultaneously solving both sub-problems.Firstly,we extend the trajectory intersection method to asynchronous cameras to resolve the limitation of traditional triangulation that requires camera synchronization.Secondly,we develop models for camera temporal information and target motion,based on imaging mechanisms and target dynamics characteristics.The parameters are optimized simultaneously to achieve trajectory reconstruction without accurate time parameters.Thirdly,we optimize the camera rotations alongside the camera time information and target motion parameters,using tighter and more continuous constraints on moving points.The reconstruction accuracy is significantly improved,especially when the camera rotations are inaccurate.Finally,the simulated and real-world experimental results demonstrate the feasibility and accuracy of the proposed method.The real-world results indicate that the proposed algorithm achieved a localization error of 112.95 m at an observation distance range of 15-20 km.
基金supported by the National Natural Science Foundation of China(No.52474172).
文摘It is important to understand the development of joints and fractures in rock masses to ensure drilling stability and blasting effectiveness.Traditional manual observation techniques for identifying and extracting fracture characteristics have been proven to be inefficient and prone to subjective interpretation.Moreover,conventional image processing algorithms and classical deep learning models often encounter difficulties in accurately identifying fracture areas,resulting in unclear contours.This study proposes an intelligent method for detecting internal fractures in mine rock masses to address these challenges.The proposed approach captures a nodal fracture map within the targeted blast area and integrates channel and spatial attention mechanisms into the ResUnet(RU)model.The channel attention mechanism dynamically recalibrates the importance of each feature channel,and the spatial attention mechanism enhances feature representation in key areas while minimizing background noise,thus improving segmentation accuracy.A dynamic serpentine convolution module is also introduced that adaptively adjusts the shape and orientation of the convolution kernel based on the local structure of the input feature map.Furthermore,this method enables the automatic extraction and quantification of borehole nodal fracture information by fitting sinusoidal curves to the boundaries of the fracture contours using the least squares method.In comparison to other advanced deep learning models,our enhanced RU demonstrates superior performance across evaluation metrics,including accuracy,pixel accuracy(PA),and intersection over union(IoU).Unlike traditional manual extraction methods,our intelligent detection approach provides considerable time and cost savings,with an average error rate of approximately 4%.This approach has the potential to greatly improve the efficiency of geological surveys of borehole fractures.
基金funded by the Ministry of Education and Training Project(code number:B2023-TCT-08).
文摘This study presents a drone-based aerial imaging method for automated rice seedling detection and counting in paddy fields.Utilizing a drone equipped with a high-resolution camera,images are captured 14 days postsowing at a consistent altitude of six meters,employing autonomous flight for uniform data acquisition.The approach effectively addresses the distinct growth patterns of both single and clustered rice seedlings at this early stage.The methodology follows a two-step process:first,the GoogleNet deep learning network identifies the location and center points of rice plants.Then,the U-Net deep learning network performs classification and counting of individual plants and clusters.This combination of deep learning models achieved a 90%accuracy rate in classifying and counting both single and clustered seedlings.To validate the method’s effectiveness,results were compared against traditional manual counting conducted by agricultural experts.The comparison revealed minimal discrepancies,with a variance of only 2–4 clumps per square meter,confirming the reliability of the proposed method.This automated approach offers significant benefits by providing an efficient,accurate,and scalable solution for monitoring seedling growth.It enables farmers to optimize fertilizer and pesticide application,improve resource allocation,and enhance overall crop management,ultimately contributing to increased agricultural productivity.
基金the Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant(No.20172005)。
文摘Closed thoracic drainage can be performed using a steel-needle-guided chest tube to treat pleural effusion or pneumothorax in clinics.However,the puncture procedure during surgery is invisible,increasing the risk of surgical failure.Therefore,it is necessary to design a visualization system for closed thoracic drainage.Augmented reality(AR)technology can assist in visualizing the internal anatomical structure and determining the insertion point on the body surface.The structure of the currently used steel-needle-guided chest tube was modified by integrating it with an ultrafine diameter camera to provide real-time visualization of the puncture process.After simulation experiments,the overall registration error of the AR method was measured to be within(3.59±0.53)mm,indicating its potential for clinical application.The ultrafine diameter camera module and improved steel-needle-guided chest tube can timely reflect the position of the needle tip in the human body.A comparative experiment showed that video guidance could improve the safety of the puncture process compared to the traditional method.Finally,a qualitative evaluation of the usability of the system was conducted through a questionnaire.This system facilitates the visualization of closed thoracic drainage puncture procedure and pro-vides an implementation scheme to enhance the accuracy and safety of the operative step,which is conducive to reducing the learning curve and improving the proficiency of the doctors.
基金Natural Science Foundation of Hunan Province,China(No.2024JJ8335)Open Topic of Hunan Geospatial Information Engineering and Technology Research Center,China(No.HNGIET2023004).
文摘The estimation of orientation parameters and correction of lens distortion are crucial problems in the field of Unmanned Aerial Vehicles(UAVs)photogrammetry.In recent years,the utilization of UAVs for aerial photogrammetry has witnessed a surge in popularity.Typically,UAVs are equipped with low-cost non-metric cameras and a Position and Orientation System(POS).Unfortunately,the Interior Orientation Parameters(IOPs)of the non-metric cameras are not fixed.Whether the lens distortions are large or small,they effect the image coordinates accordingly.Additionally,Inertial Measurement Units(IMUs)often have observation errors.To address these challenges and improve parameter estimation for UAVs Light Detection and Ranging(LiDAR)and photogrammetry,this paper analyzes the accuracy of POS observations obtained from Global Navigation Satellite System Real Time Kinematic(GNSS-RTK)and IMU data.A method that incorporates additional known conditions for parameter estimation,a series of algorithms to simultaneously solve for IOPs,Exterior Orientation Parameters(EOPs),and camera lens distortion correction parameters are proposed.Extensive experiments demonstrate that the coordinates measured by GNSS-RTK can be directly used as linear EOPs;however,angular EOP measurements from IMUs exhibit relatively large errors compared to adjustment results and require correction during the adjustment process.The IOPs of non-metric cameras vary slightly between images but need to be treated as unknown parameters in high precision applications.Furthermore,it is found that the Ebner systematic error model is sensitive to the choice of the magnification parameter of the photographic baseline length in images,it should be set as less than or equal to one third of the photographic baseline to ensure stable solutions.
