In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,par...In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,particularly in snowy environments,remains a challenge.Snow-covered roads introduce unpredictable surface conditions,occlusions,and reduced visibility,that require robust and adaptive path detection algorithms.This paper presents an enhanced road detection framework for snowy environments,leveraging Simple Framework forContrastive Learning of Visual Representations(SimCLR)for Self-Supervised pretraining,hyperparameter optimization,and uncertainty-aware object detection to improve the performance of YouOnly Look Once version 8(YOLOv8).Themodel is trained and evaluated on a custom-built dataset collected from snowy roads in Tromsø,Norway,which covers a range of snow textures,illumination conditions,and road geometries.The proposed framework achieves scores in terms of mAP@50 equal to 99%and mAP@50–95 equal to 97%,demonstrating the effectiveness of YOLOv8 for real-time road detection in extreme winter conditions.The findings contribute to the safe and reliable deployment of autonomous vehicles in Arctic environments,enabling robust decision-making in hazardous weather conditions.This research lays the groundwork for more resilient perceptionmodels in self-driving systems,paving the way for the future development of intelligent and adaptive transportation networks.展开更多
Accurate and real-time road defect detection is essential for ensuring traffic safety and infrastructure maintenance.However,existing vision-based methods often struggle with small,sparse,and low-resolution defects un...Accurate and real-time road defect detection is essential for ensuring traffic safety and infrastructure maintenance.However,existing vision-based methods often struggle with small,sparse,and low-resolution defects under complex road conditions.To address these limitations,we propose Multi-Scale Guided Detection YOLO(MGD-YOLO),a novel lightweight and high-performance object detector built upon You Only Look Once Version 5(YOLOv5).The proposed model integrates three key components:(1)a Multi-Scale Dilated Attention(MSDA)module to enhance semantic feature extraction across varying receptive fields;(2)Depthwise Separable Convolution(DSC)to reduce computational cost and improve model generalization;and(3)a Visual Global Attention Upsampling(VGAU)module that leverages high-level contextual information to refine low-level features for precise localization.Extensive experiments on three public road defect benchmarks demonstrate that MGD-YOLO outperforms state-of-the-art models in both detection accuracy and efficiency.Notably,our model achieves 87.9%accuracy in crack detection,88.3%overall precision on TD-RD dataset,while maintaining fast inference speed and a compact architecture.These results highlight the potential of MGD-YOLO for deployment in real-time,resource-constrained scenarios,paving the way for practical and scalable intelligent road maintenance systems.展开更多
Roads inevitably have defects during use,which not only seriously affect their service life but also pose a hidden danger to traffic safety.Existing algorithms for detecting road defects are unsatisfactory in terms of...Roads inevitably have defects during use,which not only seriously affect their service life but also pose a hidden danger to traffic safety.Existing algorithms for detecting road defects are unsatisfactory in terms of accuracy and generalization,so this paper proposes an algorithm based on YOLOv11.The method embeds wavelet transform convolution(WTConv)into the backbone’s C3k2 module to enhance low-frequency feature extraction while avoiding parameter bloat.Secondly,a novel multi-scale fusion diffusion network(MFDN)architecture is designed for the neck to strengthen cross-scale feature interactions,boosting detection precision.In terms of model optimization,the traditional downsampling method is discarded,and the innovative Adown(adaptive downsampling)technique is adopted,which streamlines the parameter scales while effectively mitigating the information loss problem during downsampling.Finally,in this paper,we propose Wise-PIDIoU by combining WiseIoU and MPDIoU to minimize the negative impact of low-quality anchor frames and enhance the detection capability of the model.The experimental results indicate that the proposed algorithm achieves an average detection accuracy of 86.5%for mAP@50 on the RDD2022 dataset,which is 2%higher than the original algorithm while ensuring that the amount of computation is basically unchanged.The number of parameters is reduced by 17%,and the F1 score is improved by 3%,showing better detection performance than other algorithms when facing different types of defects.The excellent performance on embedded devices proves that the algorithm also has favorable application prospects in practical inspection.展开更多
Aiming at the problems of low detection accuracy and large model size of existing object detection algorithms applied to complex road scenes,an improved you only look once version 8(YOLOv8)object detection algorithm f...Aiming at the problems of low detection accuracy and large model size of existing object detection algorithms applied to complex road scenes,an improved you only look once version 8(YOLOv8)object detection algorithm for infrared images,F-YOLOv8,is proposed.First,a spatial-to-depth network replaces the traditional backbone network's strided convolution or pooling layer.At the same time,it combines with the channel attention mechanism so that the neural network focuses on the channels with large weight values to better extract low-resolution image feature information;then an improved feature pyramid network of lightweight bidirectional feature pyramid network(L-BiFPN)is proposed,which can efficiently fuse features of different scales.In addition,a loss function of insertion of union based on the minimum point distance(MPDIoU)is introduced for bounding box regression,which obtains faster convergence speed and more accurate regression results.Experimental results on the FLIR dataset show that the improved algorithm can accurately detect infrared road targets in real time with 3%and 2.2%enhancement in mean average precision at 50%IoU(mAP50)and mean average precision at 50%—95%IoU(mAP50-95),respectively,and 38.1%,37.3%and 16.9%reduction in the number of model parameters,the model weight,and floating-point operations per second(FLOPs),respectively.To further demonstrate the detection capability of the improved algorithm,it is tested on the public dataset PASCAL VOC,and the results show that F-YOLO has excellent generalized detection performance.展开更多
There is a problem of real-time detection difficulty in road surface damage detection. This paper proposes an improved lightweight model based on you only look once version 5(YOLOv5). Firstly, this paper fully utilize...There is a problem of real-time detection difficulty in road surface damage detection. This paper proposes an improved lightweight model based on you only look once version 5(YOLOv5). Firstly, this paper fully utilized the convolutional neural network(CNN) + ghosting bottleneck(G_bneck) architecture to reduce redundant feature maps. Afterwards, we upgraded the original upsampling algorithm to content-aware reassembly of features(CARAFE) and increased the receptive field. Finally, we replaced the spatial pyramid pooling fast(SPPF) module with the basic receptive field block(Basic RFB) pooling module and added dilated convolution. After comparative experiments, we can see that the number of parameters and model size of the improved algorithm in this paper have been reduced by nearly half compared to the YOLOv5s. The frame rate per second(FPS) has been increased by 3.25 times. The mean average precision(m AP@0.5: 0.95) has increased by 8%—17% compared to other lightweight algorithms.展开更多
Road transportation plays a crucial role in society and daily life,as the functioning and durability of roads can significantly impact a nation's economic development.In the whole life cycle of the road,the emerge...Road transportation plays a crucial role in society and daily life,as the functioning and durability of roads can significantly impact a nation's economic development.In the whole life cycle of the road,the emergence of disease is unavoidable,so it is necessary to adopt relevant technical means to deal with the disease.This study comprehensively reviews the advancements in computer vision,artificial intelligence,and mobile robotics in the road domain and examines their progress and applications in road detection,diagnosis,and treatment,especially asphalt roads.Specifically,it analyzes the research progress in detecting and diagnosing surface and internal road distress and related techniques and algorithms are compared.In addition,also introduces various road gover-nance technologies,including automated repairs,intelligent construction,and path planning for crack sealing.Despite their proven effectiveness in detecting road distress,analyzing diagnoses,and planning maintenance,these technologies still confront challenges in data collection,parameter optimization,model portability,system accuracy,robustness,and real-time performance.Consequently,the integration of multidisciplinary technologies is imperative to enable the development of an integrated approach that includes road detection,diagnosis,and treatment.This paper addresses the challenges of precise defect detection,condition assessment,and unmanned construction.At the same time,the efficiency of labor liberation and road maintenance is achieved,and the automation level of the road engineering industry is improved.展开更多
The traditional You Only Look Once(YOLO)series network models often fail to extract satisfactory features for road detection,due to the limited number of defect images in the dataset.Additionally,most open-source road...The traditional You Only Look Once(YOLO)series network models often fail to extract satisfactory features for road detection,due to the limited number of defect images in the dataset.Additionally,most open-source road crack datasets contain idealized cracks that are not suitable for detecting early-stage pavement cracks with fine widths and subtle features.To address these issues,this study collected a large number of original road surface images using road detection vehicles.A large-capacity crack dataset was then constructed,with various shapes of cracks categorized as either cracks or fractures.To improve the training performance of the YOLOv5 algorithm,which showed unsatisfactory results on the original dataset,this study used median filtering to preprocess the crack images.The preprocessed images were combined to form the training set.Moreover,the Coordinate Attention(CA)attention module was integrated to further enhance the model’s training performance.The final detection model achieved a recognition accuracy of 88.9%and a recall rate of 86.1%for detecting cracks.These findings demonstrate that the use of image preprocessing technology and the introduction of the CA attention mechanism can effectively detect early-stage pavement cracks that have low contrast with the background.展开更多
Integrating Tiny Machine Learning(TinyML)with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level.Constrained devices efficiently implement a Binary Neural N...Integrating Tiny Machine Learning(TinyML)with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level.Constrained devices efficiently implement a Binary Neural Network(BNN)for road feature extraction,utilizing quantization and compression through a pruning strategy.The modifications resulted in a 28-fold decrease in memory usage and a 25%enhancement in inference speed while only experiencing a 2.5%decrease in accuracy.It showcases its superiority over conventional detection algorithms in different road image scenarios.Although constrained by computer resources and training datasets,our results indicate opportunities for future research,demonstrating that quantization and focused optimization can significantly improve machine learning models’accuracy and operational efficiency.ARM Cortex-M0 gives practical feasibility and substantial benefits while deploying our optimized BNN model on this low-power device:Advanced machine learning in edge computing.The analysis work delves into the educational significance of TinyML and its essential function in analyzing road networks using remote sensing,suggesting ways to improve smart city frameworks in road network assessment,traffic management,and autonomous vehicle navigation systems by emphasizing the importance of new technologies for maintaining and safeguarding road networks.展开更多
Surveillance cameras have been widely used for monitoring in both private and public sectors as a security measure.Close Circuits Television(CCTV)Cameras are used to surveillance and monitor the normal and anomalous i...Surveillance cameras have been widely used for monitoring in both private and public sectors as a security measure.Close Circuits Television(CCTV)Cameras are used to surveillance and monitor the normal and anomalous incidents.Real-world anomaly detection is a significant challenge due to its complex and diverse nature.It is difficult to manually analyze because vast amounts of video data have been generated through surveillance systems,and the need for automated techniques has been raised to enhance detection accuracy.This paper proposes a novel deep-stacked ensemble model integrated with a data augmentation approach called Stack Ensemble Road Anomaly Detection(SERAD).SERAD is used to detect and classify the four most happening road anomalies,such as accidents,car fires,fighting,and snatching,through road surveillance videos with high accuracy.The SERAD adapted three pre-trained Convolutional Neural Networks(CNNs)models,namely VGG19,ResNet50 and InceptionV3.The stacking technique is employed to incorporate these three models,resulting in much-improved accuracy for classifying road abnormalities compared to individual models.Additionally,it presented a custom real-world Road Anomaly Dataset(RAD)comprising a comprehensive collection of road images and videos.The experimental results demonstrate the strength and reliability of the proposed SERAD model,achieving an impressive classification accuracy of 98.7%.The results indicate that the proposed SERAD model outperforms than the individual CNN base models.展开更多
Computer-vision and deep-learning techniques are widely applied to detect,monitor,and assess pavement conditions including road crack detection.Traditional methods fail to achieve satisfactory accuracy and generalizat...Computer-vision and deep-learning techniques are widely applied to detect,monitor,and assess pavement conditions including road crack detection.Traditional methods fail to achieve satisfactory accuracy and generalization performance in for crack detection.Complex network model can generate redundant feature maps and computational complexity.Therefore,this paper proposes a novel model compression framework based on deep learning to detect road cracks,which can improve the detection efficiency and accuracy.A distillation loss function is proposed to compress the teacher model,followed by channel pruning.Meanwhile,a multi-dilation model is proposed to improve the accuracy of the model pruned.The proposed method is tested on the public database CrackForest dataset(CFD).The experimental results show that the proposed method is more efficient and accurate than other state-of-art methods.展开更多
As a vital and integral component of transportation infrastructure,pavement has a direct and tangible impact on socio-economic sustainability.In recent years,an influx of groundbreaking and state-of-the-art materials,...As a vital and integral component of transportation infrastructure,pavement has a direct and tangible impact on socio-economic sustainability.In recent years,an influx of groundbreaking and state-of-the-art materials,structures,equipment,and detection technologies related to road engineering have continually and progressively emerged,reshaping the landscape of pavement systems.There is a pressing and growing need for a timely summarization of the current research status and a clear identification of future research directions in these advanced and evolving technologies.Therefore,Journal of Road Engineering has undertaken the significant initiative of introducing a comprehensive review paper with the overarching theme of“advanced road materials,structures,equipment,and detection technologies”.This extensive and insightful review meticulously gathers and synthesizes research findings from 39 distinguished scholars,all of whom are affiliated with 19 renowned universities or research institutions specializing in the diverse and multidimensional field of highway engineering.It covers the current state and anticipates future development directions in the four major and interconnected domains of road engineering:advanced road materials,advanced road structures and performance evaluation,advanced road construction equipment and technology,and advanced road detection and assessment technologies.展开更多
Vision-based road detection is an important research topic in different areas of computer vision such as the autonomous navigation of mobile robots.In outdoor unstructured environments such as villages and deserts,the...Vision-based road detection is an important research topic in different areas of computer vision such as the autonomous navigation of mobile robots.In outdoor unstructured environments such as villages and deserts,the roads are usually not well-paved and have variant colors or texture distributions.Traditional region- or edge-based approaches,however,are effective only in specific environments,and most of them have weak adaptability to varying road types and appearances.In this paper we describe a novel top-down based hybrid algorithm which properly combines both region and edge cues from the images.The main difference between our proposed algorithm and previous ones is that,before road detection,an off-line scene classifier is efficiently learned by both low- and high-level image cues to predict the unstructured road model.This scene classification can be considered a decision process which guides the selection of the optimal solution from region- or edge-based approaches to detect the road.Moreover,a temporal smoothing mechanism is incorporated,which further makes both model prediction and region classification more stable.Experimental results demonstrate that compared with traditional region- and edge-based algorithms,our algorithm is more robust in detecting the road areas with diverse road types and varying appearances in unstructured conditions.展开更多
Automatic road damage detection using image processing is an important aspect of road maintenance.It is also a challenging problem due to the inhomogeneity of road damage and complicated background in the road images....Automatic road damage detection using image processing is an important aspect of road maintenance.It is also a challenging problem due to the inhomogeneity of road damage and complicated background in the road images.In recent years,deep convolutional neural network based methods have been used to address the challenges of road damage detection and classification.In this paper,we propose a new approach to address those challenges.This approach uses densely connected convolution networks as the backbone of the Mask R-CNN to effectively extract image feature,a feature pyramid network for combining multiple scales features,a region proposal network to generate the road damage region,and a fully convolutional neural network to classify the road damage region and refine the region bounding box.This method can not only detect and classify the road damage,but also create a mask of the road damage.Experimental results show that the proposed approach can achieve better results compared with other existing methods.展开更多
Road marking detection is an important branch in autonomous driving,understanding the road information.In recent years,deep-learning-based semantic segmentation methods for road marking detection have been arising sin...Road marking detection is an important branch in autonomous driving,understanding the road information.In recent years,deep-learning-based semantic segmentation methods for road marking detection have been arising since they can generalize detection result well under complicated environments and hold rich pixel-level semantic information.Nevertheless,the previous methods mostly study the training process of the segmentation network,while omitting the time cost of manually annotating pixel-level data.Besides,the pixel-level semantic segmentation results need to be fitted into more reliable and compact models so that geometrical information of road markings can be explicitly obtained.In order to tackle the above problems,this paper describes a semantic segmentation-based road marking detection method using around view monitoring system.A semiautomatic semantic annotation platform is developed,which exploits an auxiliary segmentation graph to speed up the annotation process while guaranteeing the annotation accuracy.A segmentation-based detection module is also described,which models the semantic segmentation results for the more robust and compact analysis.The proposed detection module is composed of three parts:vote-based segmentation fusion filtering,graph-based road marking clustering,and road-marking fitting.Experiments under various scenarios show that the semantic segmentation-based detection method can achieve accurate,robust,and real-time detection performance.展开更多
Video processing is one challenge in collecting vehicle trajectories from unmanned aerial vehicle(UAV) and road boundary estimation is one way to improve the video processing algorithms. However, current methods do no...Video processing is one challenge in collecting vehicle trajectories from unmanned aerial vehicle(UAV) and road boundary estimation is one way to improve the video processing algorithms. However, current methods do not work well for low volume road, which is not well-marked and with noises such as vehicle tracks. A fusion-based method termed Dempster-Shafer-based road detection(DSRD) is proposed to address this issue. This method detects road boundary by combining multiple information sources using Dempster-Shafer theory(DST). In order to test the performance of the proposed method, two field experiments were conducted, one of which was on a highway partially covered by snow and another was on a dense traffic highway. The results show that DSRD is robust and accurate, whose detection rates are 100% and 99.8% compared with manual detection results. Then, DSRD is adopted to improve UAV video processing algorithm, and the vehicle detection and tracking rate are improved by 2.7% and 5.5%,respectively. Also, the computation time has decreased by 5% and 8.3% for two experiments, respectively.展开更多
This study aimed to propose road crack detection method based on infrared image fusion technology.By analyzing the characteristics of road crack images,this method uses a variety of infrared image fusion methods to pr...This study aimed to propose road crack detection method based on infrared image fusion technology.By analyzing the characteristics of road crack images,this method uses a variety of infrared image fusion methods to process different types of images.The use of this method allows the detection of road cracks,which not only reduces the professional requirements for inspectors,but also improves the accuracy of road crack detection.Based on infrared image processing technology,on the basis of in-depth analysis of infrared image features,a road crack detection method is proposed,which can accurately identify the road crack location,direction,length,and other characteristic information.Experiments showed that this method has a good effect,and can meet the requirement of road crack detection.展开更多
With the continuous development of remote sensing(RS)technology,the surface information can be collected conveniently and quickly by using the popular unmanned aerial vehicle(UAV).The application of UAV low altitude R...With the continuous development of remote sensing(RS)technology,the surface information can be collected conveniently and quickly by using the popular unmanned aerial vehicle(UAV).The application of UAV low altitude RS technology in road safety in intelligent area has certain practical significance.It can provide safety warning for most drivers,and provide auxiliary decision-making for the road supervision department.Through the collection,processing,calculation and analysis of the road image,the UAV can find out the road obstacles with potential safety hazards,identify the road pit,calculate the radius and depth of the road pit through the digital mapping system,predict the accident risk according to different speed and provide scientific basis for the road safety monitoring.At the same time,UAV can provide repair scheme for damaged roads,estimate the quantity of materials needed for repair,and achieve the target of resource saving and efficiency improvement.The experimental results show that the UAV can not only provide scientific prediction information for driving safety,but also provide relatively accurate material consumption for road repair.展开更多
Aiming at the problems of high complexity and low accuracy of existing visibility detection methods,a road visibility detection method based on monitoring images is proposed.Firstly,the transmittance of dark and brigh...Aiming at the problems of high complexity and low accuracy of existing visibility detection methods,a road visibility detection method based on monitoring images is proposed.Firstly,the transmittance of dark and bright primary colors is obtained by the theory of dark and bright primary color prior.Then,the atmospheric light value and atmospheric transmittance are optimized by using adaptive fog removal weight and adaptive filtering window,and the transmittance of the first and last end points of the lane line is one-toone corresponding to the optimized dark and bright primary transmittance.Finally,the atmospheric extinction coefficient and visibility are calculated by combining the distance between the end and end of the lane line.The experimental results show that this method can achieve high precision detection within 100-600m,and the relative error is less than 10%.Compared with other methods,the detection efficiency of this method is faster,the accuracy is higher and the realization is easier.展开更多
Rapid and high-precision speed bump detection is critical for autonomous driving and road safety,yet it faces challenges from non-standard appearances and complex environments.To address this issue,this study proposes...Rapid and high-precision speed bump detection is critical for autonomous driving and road safety,yet it faces challenges from non-standard appearances and complex environments.To address this issue,this study proposes a you only look once(YOLO)algorithm for speed bump detection(SPD-YOLO),a lightweight model based on YOLO11s that integrates three core innova-tive modules to balance detection precision and computational efficiency:it replaces YOLO11s’original backbone with StarNet,which uses‘star operations’to map features into high-dimensional nonlinear spaces for enhanced feature representation while maintaining computational efficiency;its neck incorporates context feature calibration(CFC)and spatial feature calibration(SFC)to improve detection performance without significant computational overhead;and its detection head adopts a lightweight shared convolutional detection(LSCD)structure combined with GroupNorm,minimizing computational complexity while preserving multi-scale feature fusion efficacy.Experi-ments on a custom speed bump dataset show SPD-YOLO achieves a mean average precision(mAP)of 79.9%,surpassing YOLO11s by 1.3%and YOLO12s by 1.2%while reducing parameters by 26.3%and floating-point operations per second(FLOPs)by 29.5%,enabling real-time deploy-ment on resource-constrained platforms.展开更多
文摘In recent years,advancements in autonomous vehicle technology have accelerated,promising safer and more efficient transportation systems.However,achieving fully autonomous driving in challenging weather conditions,particularly in snowy environments,remains a challenge.Snow-covered roads introduce unpredictable surface conditions,occlusions,and reduced visibility,that require robust and adaptive path detection algorithms.This paper presents an enhanced road detection framework for snowy environments,leveraging Simple Framework forContrastive Learning of Visual Representations(SimCLR)for Self-Supervised pretraining,hyperparameter optimization,and uncertainty-aware object detection to improve the performance of YouOnly Look Once version 8(YOLOv8).Themodel is trained and evaluated on a custom-built dataset collected from snowy roads in Tromsø,Norway,which covers a range of snow textures,illumination conditions,and road geometries.The proposed framework achieves scores in terms of mAP@50 equal to 99%and mAP@50–95 equal to 97%,demonstrating the effectiveness of YOLOv8 for real-time road detection in extreme winter conditions.The findings contribute to the safe and reliable deployment of autonomous vehicles in Arctic environments,enabling robust decision-making in hazardous weather conditions.This research lays the groundwork for more resilient perceptionmodels in self-driving systems,paving the way for the future development of intelligent and adaptive transportation networks.
基金supported by Chengdu Jincheng College under the General Research Project Program(Project No.JG2024-1199)titled“Research on the Training Mechanism of Undergraduate Innovation Ability Based on Deep Integration of AI Industry-Education Collaboration”.
文摘Accurate and real-time road defect detection is essential for ensuring traffic safety and infrastructure maintenance.However,existing vision-based methods often struggle with small,sparse,and low-resolution defects under complex road conditions.To address these limitations,we propose Multi-Scale Guided Detection YOLO(MGD-YOLO),a novel lightweight and high-performance object detector built upon You Only Look Once Version 5(YOLOv5).The proposed model integrates three key components:(1)a Multi-Scale Dilated Attention(MSDA)module to enhance semantic feature extraction across varying receptive fields;(2)Depthwise Separable Convolution(DSC)to reduce computational cost and improve model generalization;and(3)a Visual Global Attention Upsampling(VGAU)module that leverages high-level contextual information to refine low-level features for precise localization.Extensive experiments on three public road defect benchmarks demonstrate that MGD-YOLO outperforms state-of-the-art models in both detection accuracy and efficiency.Notably,our model achieves 87.9%accuracy in crack detection,88.3%overall precision on TD-RD dataset,while maintaining fast inference speed and a compact architecture.These results highlight the potential of MGD-YOLO for deployment in real-time,resource-constrained scenarios,paving the way for practical and scalable intelligent road maintenance systems.
文摘Roads inevitably have defects during use,which not only seriously affect their service life but also pose a hidden danger to traffic safety.Existing algorithms for detecting road defects are unsatisfactory in terms of accuracy and generalization,so this paper proposes an algorithm based on YOLOv11.The method embeds wavelet transform convolution(WTConv)into the backbone’s C3k2 module to enhance low-frequency feature extraction while avoiding parameter bloat.Secondly,a novel multi-scale fusion diffusion network(MFDN)architecture is designed for the neck to strengthen cross-scale feature interactions,boosting detection precision.In terms of model optimization,the traditional downsampling method is discarded,and the innovative Adown(adaptive downsampling)technique is adopted,which streamlines the parameter scales while effectively mitigating the information loss problem during downsampling.Finally,in this paper,we propose Wise-PIDIoU by combining WiseIoU and MPDIoU to minimize the negative impact of low-quality anchor frames and enhance the detection capability of the model.The experimental results indicate that the proposed algorithm achieves an average detection accuracy of 86.5%for mAP@50 on the RDD2022 dataset,which is 2%higher than the original algorithm while ensuring that the amount of computation is basically unchanged.The number of parameters is reduced by 17%,and the F1 score is improved by 3%,showing better detection performance than other algorithms when facing different types of defects.The excellent performance on embedded devices proves that the algorithm also has favorable application prospects in practical inspection.
基金supported by the National Natural Science Foundation of China(No.62103298)。
文摘Aiming at the problems of low detection accuracy and large model size of existing object detection algorithms applied to complex road scenes,an improved you only look once version 8(YOLOv8)object detection algorithm for infrared images,F-YOLOv8,is proposed.First,a spatial-to-depth network replaces the traditional backbone network's strided convolution or pooling layer.At the same time,it combines with the channel attention mechanism so that the neural network focuses on the channels with large weight values to better extract low-resolution image feature information;then an improved feature pyramid network of lightweight bidirectional feature pyramid network(L-BiFPN)is proposed,which can efficiently fuse features of different scales.In addition,a loss function of insertion of union based on the minimum point distance(MPDIoU)is introduced for bounding box regression,which obtains faster convergence speed and more accurate regression results.Experimental results on the FLIR dataset show that the improved algorithm can accurately detect infrared road targets in real time with 3%and 2.2%enhancement in mean average precision at 50%IoU(mAP50)and mean average precision at 50%—95%IoU(mAP50-95),respectively,and 38.1%,37.3%and 16.9%reduction in the number of model parameters,the model weight,and floating-point operations per second(FLOPs),respectively.To further demonstrate the detection capability of the improved algorithm,it is tested on the public dataset PASCAL VOC,and the results show that F-YOLO has excellent generalized detection performance.
基金supported by the Shanghai Sailing Program,China (No.20YF1447600)the Research Start-Up Project of Shanghai Institute of Technology (No.YJ2021-60)+1 种基金the Collaborative Innovation Project of Shanghai Institute of Technology (No.XTCX2020-12)the Science and Technology Talent Development Fund for Young and Middle-Aged Teachers at Shanghai Institute of Technology (No.ZQ2022-6)。
文摘There is a problem of real-time detection difficulty in road surface damage detection. This paper proposes an improved lightweight model based on you only look once version 5(YOLOv5). Firstly, this paper fully utilized the convolutional neural network(CNN) + ghosting bottleneck(G_bneck) architecture to reduce redundant feature maps. Afterwards, we upgraded the original upsampling algorithm to content-aware reassembly of features(CARAFE) and increased the receptive field. Finally, we replaced the spatial pyramid pooling fast(SPPF) module with the basic receptive field block(Basic RFB) pooling module and added dilated convolution. After comparative experiments, we can see that the number of parameters and model size of the improved algorithm in this paper have been reduced by nearly half compared to the YOLOv5s. The frame rate per second(FPS) has been increased by 3.25 times. The mean average precision(m AP@0.5: 0.95) has increased by 8%—17% compared to other lightweight algorithms.
基金supported by the National Key Research and Development Program of China (No.2021YFB2601000)National Natural Science Foundation of China (Nos.52078049,52378431)+2 种基金Fundamental Research Funds for the Central Universities,CHD (Nos.300102210302,300102210118)the 111 Proj-ect of Sustainable Transportation for Urban Agglomeration in Western China (No.B20035)Natural Science Foundation of Shaanxi Province of China (No.S2022-JM-193).
文摘Road transportation plays a crucial role in society and daily life,as the functioning and durability of roads can significantly impact a nation's economic development.In the whole life cycle of the road,the emergence of disease is unavoidable,so it is necessary to adopt relevant technical means to deal with the disease.This study comprehensively reviews the advancements in computer vision,artificial intelligence,and mobile robotics in the road domain and examines their progress and applications in road detection,diagnosis,and treatment,especially asphalt roads.Specifically,it analyzes the research progress in detecting and diagnosing surface and internal road distress and related techniques and algorithms are compared.In addition,also introduces various road gover-nance technologies,including automated repairs,intelligent construction,and path planning for crack sealing.Despite their proven effectiveness in detecting road distress,analyzing diagnoses,and planning maintenance,these technologies still confront challenges in data collection,parameter optimization,model portability,system accuracy,robustness,and real-time performance.Consequently,the integration of multidisciplinary technologies is imperative to enable the development of an integrated approach that includes road detection,diagnosis,and treatment.This paper addresses the challenges of precise defect detection,condition assessment,and unmanned construction.At the same time,the efficiency of labor liberation and road maintenance is achieved,and the automation level of the road engineering industry is improved.
基金jointly supported by the National Natural Science Foundation of China(No.52308332)the China Postdoctoral Science Foundation(Grant No.2022M712787).
文摘The traditional You Only Look Once(YOLO)series network models often fail to extract satisfactory features for road detection,due to the limited number of defect images in the dataset.Additionally,most open-source road crack datasets contain idealized cracks that are not suitable for detecting early-stage pavement cracks with fine widths and subtle features.To address these issues,this study collected a large number of original road surface images using road detection vehicles.A large-capacity crack dataset was then constructed,with various shapes of cracks categorized as either cracks or fractures.To improve the training performance of the YOLOv5 algorithm,which showed unsatisfactory results on the original dataset,this study used median filtering to preprocess the crack images.The preprocessed images were combined to form the training set.Moreover,the Coordinate Attention(CA)attention module was integrated to further enhance the model’s training performance.The final detection model achieved a recognition accuracy of 88.9%and a recall rate of 86.1%for detecting cracks.These findings demonstrate that the use of image preprocessing technology and the introduction of the CA attention mechanism can effectively detect early-stage pavement cracks that have low contrast with the background.
基金supported by the National Natural Science Foundation of China(61170147)Scientific Research Project of Zhejiang Provincial Department of Education in China(Y202146796)+2 种基金Natural Science Foundation of Zhejiang Province in China(LTY22F020003)Wenzhou Major Scientific and Technological Innovation Project of China(ZG2021029)Scientific and Technological Projects of Henan Province in China(202102210172).
文摘Integrating Tiny Machine Learning(TinyML)with edge computing in remotely sensed images enhances the capabilities of road anomaly detection on a broader level.Constrained devices efficiently implement a Binary Neural Network(BNN)for road feature extraction,utilizing quantization and compression through a pruning strategy.The modifications resulted in a 28-fold decrease in memory usage and a 25%enhancement in inference speed while only experiencing a 2.5%decrease in accuracy.It showcases its superiority over conventional detection algorithms in different road image scenarios.Although constrained by computer resources and training datasets,our results indicate opportunities for future research,demonstrating that quantization and focused optimization can significantly improve machine learning models’accuracy and operational efficiency.ARM Cortex-M0 gives practical feasibility and substantial benefits while deploying our optimized BNN model on this low-power device:Advanced machine learning in edge computing.The analysis work delves into the educational significance of TinyML and its essential function in analyzing road networks using remote sensing,suggesting ways to improve smart city frameworks in road network assessment,traffic management,and autonomous vehicle navigation systems by emphasizing the importance of new technologies for maintaining and safeguarding road networks.
基金funded by the King Saud University,Riyadh,Saudi Arabia for funding this work through Researchers Supporting Project Number-RSPD2024R893.
文摘Surveillance cameras have been widely used for monitoring in both private and public sectors as a security measure.Close Circuits Television(CCTV)Cameras are used to surveillance and monitor the normal and anomalous incidents.Real-world anomaly detection is a significant challenge due to its complex and diverse nature.It is difficult to manually analyze because vast amounts of video data have been generated through surveillance systems,and the need for automated techniques has been raised to enhance detection accuracy.This paper proposes a novel deep-stacked ensemble model integrated with a data augmentation approach called Stack Ensemble Road Anomaly Detection(SERAD).SERAD is used to detect and classify the four most happening road anomalies,such as accidents,car fires,fighting,and snatching,through road surveillance videos with high accuracy.The SERAD adapted three pre-trained Convolutional Neural Networks(CNNs)models,namely VGG19,ResNet50 and InceptionV3.The stacking technique is employed to incorporate these three models,resulting in much-improved accuracy for classifying road abnormalities compared to individual models.Additionally,it presented a custom real-world Road Anomaly Dataset(RAD)comprising a comprehensive collection of road images and videos.The experimental results demonstrate the strength and reliability of the proposed SERAD model,achieving an impressive classification accuracy of 98.7%.The results indicate that the proposed SERAD model outperforms than the individual CNN base models.
基金supported in part by the Jiangsu Province Construction System Science and Technology Project(No.2024ZD056)the Research Development Fund of Xi’an Jiaotong-Liverpool University(No.RDF-24-01-097).
文摘Computer-vision and deep-learning techniques are widely applied to detect,monitor,and assess pavement conditions including road crack detection.Traditional methods fail to achieve satisfactory accuracy and generalization performance in for crack detection.Complex network model can generate redundant feature maps and computational complexity.Therefore,this paper proposes a novel model compression framework based on deep learning to detect road cracks,which can improve the detection efficiency and accuracy.A distillation loss function is proposed to compress the teacher model,followed by channel pruning.Meanwhile,a multi-dilation model is proposed to improve the accuracy of the model pruned.The proposed method is tested on the public database CrackForest dataset(CFD).The experimental results show that the proposed method is more efficient and accurate than other state-of-art methods.
基金support from the European Union's Horizon 2020 Research and Innovation Program under the Marie Skłodowska-Curie grant agreement No.101024139,the RILEM technical committee TC 279 WMR(valorisation of waste and secondary materials for roads),RILEM technical committee TC-264 RAP(asphalt pavement recycling)the Swiss National Science Foundation(SNF)grant 205121_178991/1 for the project titled“Urban Mining for Low Noise Urban Roads and Optimized Design of Street Canyons”,National Natural Science Foundation of China(No.51808462,51978547,52005048,52108394,52178414,52208420,52278448,52308447,52378429)+9 种基金China Postdoctoral Science Foundation(No.2023M730356)National Key R&D Program of China(No.2021YFB2601302)Natural Science Basic Research Program of Shaanxi(Program No.2023-JC-QN-0472)Postdoctoral Science Foundation of Anhui Province(2022B627)Shaanxi Provincial Science and Technology Department(No.2022 PT30)Key Technological Special Project of Xinxiang City(No.22ZD013)Key Laboratory of Intelligent Manufacturing of Construction Machinery(No.IMCM2021KF02)the Applied Basic Research Project of Sichuan Science and Technology Department(Free Exploration Type)(Grant No.2020YJ0039)Key R&D Support Plan of Chengdu Science and Technology Project-Technology Innovation R&D Project(Grant No.2019-YF05-00002-SN)the China Postdoctoral Science Foundation(Grant No.2018M643520).
文摘As a vital and integral component of transportation infrastructure,pavement has a direct and tangible impact on socio-economic sustainability.In recent years,an influx of groundbreaking and state-of-the-art materials,structures,equipment,and detection technologies related to road engineering have continually and progressively emerged,reshaping the landscape of pavement systems.There is a pressing and growing need for a timely summarization of the current research status and a clear identification of future research directions in these advanced and evolving technologies.Therefore,Journal of Road Engineering has undertaken the significant initiative of introducing a comprehensive review paper with the overarching theme of“advanced road materials,structures,equipment,and detection technologies”.This extensive and insightful review meticulously gathers and synthesizes research findings from 39 distinguished scholars,all of whom are affiliated with 19 renowned universities or research institutions specializing in the diverse and multidimensional field of highway engineering.It covers the current state and anticipates future development directions in the four major and interconnected domains of road engineering:advanced road materials,advanced road structures and performance evaluation,advanced road construction equipment and technology,and advanced road detection and assessment technologies.
文摘Vision-based road detection is an important research topic in different areas of computer vision such as the autonomous navigation of mobile robots.In outdoor unstructured environments such as villages and deserts,the roads are usually not well-paved and have variant colors or texture distributions.Traditional region- or edge-based approaches,however,are effective only in specific environments,and most of them have weak adaptability to varying road types and appearances.In this paper we describe a novel top-down based hybrid algorithm which properly combines both region and edge cues from the images.The main difference between our proposed algorithm and previous ones is that,before road detection,an off-line scene classifier is efficiently learned by both low- and high-level image cues to predict the unstructured road model.This scene classification can be considered a decision process which guides the selection of the optimal solution from region- or edge-based approaches to detect the road.Moreover,a temporal smoothing mechanism is incorporated,which further makes both model prediction and region classification more stable.Experimental results demonstrate that compared with traditional region- and edge-based algorithms,our algorithm is more robust in detecting the road areas with diverse road types and varying appearances in unstructured conditions.
基金supported by the School Doctoral Fund of Zhengzhou University of Light Industry No.2015BSJJ051.
文摘Automatic road damage detection using image processing is an important aspect of road maintenance.It is also a challenging problem due to the inhomogeneity of road damage and complicated background in the road images.In recent years,deep convolutional neural network based methods have been used to address the challenges of road damage detection and classification.In this paper,we propose a new approach to address those challenges.This approach uses densely connected convolution networks as the backbone of the Mask R-CNN to effectively extract image feature,a feature pyramid network for combining multiple scales features,a region proposal network to generate the road damage region,and a fully convolutional neural network to classify the road damage region and refine the region bounding box.This method can not only detect and classify the road damage,but also create a mask of the road damage.Experimental results show that the proposed approach can achieve better results compared with other existing methods.
基金the National Natural Science Foundation of China(Nos.U1764264 and 61873165)the Shanghai Automotive Industry Science and Technology Development Foundation(No.1807)。
文摘Road marking detection is an important branch in autonomous driving,understanding the road information.In recent years,deep-learning-based semantic segmentation methods for road marking detection have been arising since they can generalize detection result well under complicated environments and hold rich pixel-level semantic information.Nevertheless,the previous methods mostly study the training process of the segmentation network,while omitting the time cost of manually annotating pixel-level data.Besides,the pixel-level semantic segmentation results need to be fitted into more reliable and compact models so that geometrical information of road markings can be explicitly obtained.In order to tackle the above problems,this paper describes a semantic segmentation-based road marking detection method using around view monitoring system.A semiautomatic semantic annotation platform is developed,which exploits an auxiliary segmentation graph to speed up the annotation process while guaranteeing the annotation accuracy.A segmentation-based detection module is also described,which models the semantic segmentation results for the more robust and compact analysis.The proposed detection module is composed of three parts:vote-based segmentation fusion filtering,graph-based road marking clustering,and road-marking fitting.Experiments under various scenarios show that the semantic segmentation-based detection method can achieve accurate,robust,and real-time detection performance.
基金Project(2009AA11Z220)supported by the National High Technology Research and Development Program of China
文摘Video processing is one challenge in collecting vehicle trajectories from unmanned aerial vehicle(UAV) and road boundary estimation is one way to improve the video processing algorithms. However, current methods do not work well for low volume road, which is not well-marked and with noises such as vehicle tracks. A fusion-based method termed Dempster-Shafer-based road detection(DSRD) is proposed to address this issue. This method detects road boundary by combining multiple information sources using Dempster-Shafer theory(DST). In order to test the performance of the proposed method, two field experiments were conducted, one of which was on a highway partially covered by snow and another was on a dense traffic highway. The results show that DSRD is robust and accurate, whose detection rates are 100% and 99.8% compared with manual detection results. Then, DSRD is adopted to improve UAV video processing algorithm, and the vehicle detection and tracking rate are improved by 2.7% and 5.5%,respectively. Also, the computation time has decreased by 5% and 8.3% for two experiments, respectively.
文摘This study aimed to propose road crack detection method based on infrared image fusion technology.By analyzing the characteristics of road crack images,this method uses a variety of infrared image fusion methods to process different types of images.The use of this method allows the detection of road cracks,which not only reduces the professional requirements for inspectors,but also improves the accuracy of road crack detection.Based on infrared image processing technology,on the basis of in-depth analysis of infrared image features,a road crack detection method is proposed,which can accurately identify the road crack location,direction,length,and other characteristic information.Experiments showed that this method has a good effect,and can meet the requirement of road crack detection.
基金National Natural Science Foundation(51708098)Key Laboratory Project of National Bureau of Surveying and Mapping Geographic Information for Watershed Ecology and Geographic Environment Monitoring(WE2016018)。
文摘With the continuous development of remote sensing(RS)technology,the surface information can be collected conveniently and quickly by using the popular unmanned aerial vehicle(UAV).The application of UAV low altitude RS technology in road safety in intelligent area has certain practical significance.It can provide safety warning for most drivers,and provide auxiliary decision-making for the road supervision department.Through the collection,processing,calculation and analysis of the road image,the UAV can find out the road obstacles with potential safety hazards,identify the road pit,calculate the radius and depth of the road pit through the digital mapping system,predict the accident risk according to different speed and provide scientific basis for the road safety monitoring.At the same time,UAV can provide repair scheme for damaged roads,estimate the quantity of materials needed for repair,and achieve the target of resource saving and efficiency improvement.The experimental results show that the UAV can not only provide scientific prediction information for driving safety,but also provide relatively accurate material consumption for road repair.
文摘Aiming at the problems of high complexity and low accuracy of existing visibility detection methods,a road visibility detection method based on monitoring images is proposed.Firstly,the transmittance of dark and bright primary colors is obtained by the theory of dark and bright primary color prior.Then,the atmospheric light value and atmospheric transmittance are optimized by using adaptive fog removal weight and adaptive filtering window,and the transmittance of the first and last end points of the lane line is one-toone corresponding to the optimized dark and bright primary transmittance.Finally,the atmospheric extinction coefficient and visibility are calculated by combining the distance between the end and end of the lane line.The experimental results show that this method can achieve high precision detection within 100-600m,and the relative error is less than 10%.Compared with other methods,the detection efficiency of this method is faster,the accuracy is higher and the realization is easier.
文摘Rapid and high-precision speed bump detection is critical for autonomous driving and road safety,yet it faces challenges from non-standard appearances and complex environments.To address this issue,this study proposes a you only look once(YOLO)algorithm for speed bump detection(SPD-YOLO),a lightweight model based on YOLO11s that integrates three core innova-tive modules to balance detection precision and computational efficiency:it replaces YOLO11s’original backbone with StarNet,which uses‘star operations’to map features into high-dimensional nonlinear spaces for enhanced feature representation while maintaining computational efficiency;its neck incorporates context feature calibration(CFC)and spatial feature calibration(SFC)to improve detection performance without significant computational overhead;and its detection head adopts a lightweight shared convolutional detection(LSCD)structure combined with GroupNorm,minimizing computational complexity while preserving multi-scale feature fusion efficacy.Experi-ments on a custom speed bump dataset show SPD-YOLO achieves a mean average precision(mAP)of 79.9%,surpassing YOLO11s by 1.3%and YOLO12s by 1.2%while reducing parameters by 26.3%and floating-point operations per second(FLOPs)by 29.5%,enabling real-time deploy-ment on resource-constrained platforms.
