Pavement distress detection and classification based on YOLO network

Du, Yuchuan; Pan, Ning; Zhang, Xu; Deng, Fuwen; Shen, Yu; Ke, Hua

doi:10.1080/10298436.2020.1714047

Cited by 215 publications

(86 citation statements)

References 24 publications

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“…As an additional module of Neck, SPP uses multi-scale pooling to effectively improve the detection accuracy of the training model, and uses a series of up-sampling and down-sampling of PANet (Path Aggregation Network) to fuse the features of each target scale. Compared with YOLO [32][33][34], YOLOv2 [35], YOLOv3 [36,37], YOLOv4 greatly improves the detection accuracy of the model while ensuring speed, and has the highest accuracy among all current real-time target detection algorithms [38], the map on the coco dataset is 43.5%. YOLOv4 network structure is shown in Figure 4.…”

Section: Yolov4 Detection Networkmentioning

confidence: 99%

Recognition Method of Digital Meter Readings in Substation Based on Connected Domain Analysis Algorithm

et al. 2021

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Aiming at the problem that the number and decimal point of digital instruments in substations are prone to misdetection and missed detection, a method of digital meter readings in a substation based on connected domain analysis algorithm is proposed. This method uses Faster R-CNN (Faster Region Convolutional Neural Network) as a positioning network to localize the dial area, and after acquiring the partial image, it enhances the useful information of the digital area. YOLOv4 (You Only Look Once) convolutional neural network is used as the detector to detect the digital area. The purpose is to distinguish the numbers and obtain the digital area that may contain a decimal point or no decimal point at the tail. Combined with the connected domain analysis algorithm, the difference between the number of connected domain categories and the area ratio of the digital area is analyzed, and the judgment of the decimal point is realized. The method reduces the problem of mutual interference among categories when detecting YOLOv4. The experimental results show that the method improves the detection accuracy of the algorithm.

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Section: Yolov4 Detection Networkmentioning

confidence: 99%

Recognition Method of Digital Meter Readings in Substation Based on Connected Domain Analysis Algorithm

et al. 2021

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“…In addition to the SSM method, the bounding boxes are generated to locate the region of interest by the object detection algorithm named "you only look once version 3 (YOLOv3) [38]." YOLO is one of the real-time deep CNN methods that aim at detecting objects and is widely applied in traffic management.…”

Section: Journal Of Advanced Transportationmentioning

confidence: 99%

Dynamic Pavement Distress Image Stitching Based on Fine-Grained Feature Matching

Weng

Liu

et al. 2020

Journal of Advanced Transportation

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Camera-based pavement distress detection plays an important role in pavement maintenance. Duplicate collections for the same distress and multiple overlaps of defects are both practical problems that greatly affect the detection results. In this paper, we propose a fine-grained feature-matching and image-stitching method for pavement distress detection to eliminate duplications and visually demonstrates local pavement distress. The original images are processed through a hierarchical structure, including rough data filtering, feature matching, and image stitching. The original data are firstly filtered based on the global position system (GPS) information, which can avoid full-dataset comparison and improve the calculating efficiency. A scale-invariant feature transform is introduced for feature matching based on the extracted key regions using spectral saliency mapping and bounding boxes. Two parameters: the mean Euclidean distance (MEuD) and the matching rate (MCR) are constructed to identify the duplication between two images. A support vector machine is then applied to determine the threshold of MEuD and MCR. This paper further discusses the correlation between the sampling frequency and the number of detection vehicles. The method provided can effectively solve the problem of duplications in pavement distress detection and enhances the feasibility of multivehicle pavement distress detection based on images.

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“…Above all, YOLO version 3 (YOLOv3) is a mainstream method, and it has been widely used in remote sensing [25,26], agriculture [27], and energy [28]. It has also been successfully applied in transportation infrastructure, e.g., for the detection of pavement potholes and cracking [28][29][30]. Currently, the latest YOLO version 4 (YOLOv4) [31] and YOLO version 5 (YOLOv5) [32] have become more effective for object detection by integrating the most advanced methods.…”

Section: Introductionmentioning

confidence: 99%

Application of Combining YOLO Models and 3D GPR Images in Road Detection and Maintenance

Liu

Wu²,

et al. 2021

Remote Sensing

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Improving the detection efficiency and maintenance benefits is one of the greatest challenges in road testing and maintenance. To address this problem, this paper presents a method for combining the you only look once (YOLO) series with 3D ground-penetrating radar (GPR) images to recognize the internal defects in asphalt pavement and compares the effectiveness of traditional detection and GPR detection by evaluating the maintenance benefits. First, traditional detection is conducted to survey and summarize the surface conditions of tested roads, which are missing the internal information. Therefore, GPR detection is implemented to acquire the images of concealed defects. Then, the YOLOv5 model with the most even performance of the six selected models is applied to achieve the rapid identification of road defects. Finally, the benefits evaluation of maintenance programs based on these two detection methods is conducted from economic and environmental perspectives. The results demonstrate that the economic scores are improved and the maintenance cost is reduced by $49,398/km based on GPR detection; the energy consumption and carbon emissions are reduced by 792,106 MJ/km (16.94%) and 56,289 kg/km (16.91%), respectively, all of which indicates the effectiveness of 3D GPR in pavement detection and maintenance.

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Pavement distress detection and classification based on YOLO network

Cited by 215 publications

References 24 publications

Recognition Method of Digital Meter Readings in Substation Based on Connected Domain Analysis Algorithm

Recognition Method of Digital Meter Readings in Substation Based on Connected Domain Analysis Algorithm

Dynamic Pavement Distress Image Stitching Based on Fine-Grained Feature Matching

Application of Combining YOLO Models and 3D GPR Images in Road Detection and Maintenance

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