A Depth Camera-Based Intelligent Method for Identifying and Quantifying Pavement Diseases

Bai, Hao; Hu, Xiangyu; Chen, Fei; Liao, Zhiyong; Li, Kai; Ran, Guangjiong; Wei, Fengni

doi:10.1155/2022/4992321

Cited by 2 publications

(1 citation statement)

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“…Based on the limitations of manual inspection and the advancements in computer technology, several researchers have proposed semiautomated defect detection algorithms that rely on computerassisted processing. These algorithms utilize image processing techniques, such as grayscale thresholding, edge detection, and wavelet transformation to accomplish the detection task [6][7][8][9]. This algorithm requires a large number of human resources and equipment to capture images and videos of road defects.…”

Section: Introductionmentioning

confidence: 99%

An efficient method of pavement distress detection based on improved YOLOv7

Xiao

et al. 2023

Meas. Sci. Technol.

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Pavements play a pivotal role in infrastructure construction, so Pavement Distress Detection (PDD) will greatly affect pavement service life and vehicle operation safety. Traditional manual detection and computer detection methods have such disadvantages as low efficiency, high cost and error-proneness. Thus, they are not suitable for high-speed detection tasks with a large number of defects. The defect detection method based on deep learning can achieve end-to-end target detection, generalize and monitor targets in real time. On such basis, this paper has proposed an efficient method of PDD based on improved YOLOv7. YOLOv7, which is the best-performing object detection model in the YOLO series, is known for its high efficiency, strong scalability, and support for panoramic detection. It lays a solid foundation for enhancing PDD models. In this paper, this model will be improved from model speed and accuracy. Firstly, SimAM attention module is employed to weight feature images, which has greatly improved model accuracy. Secondly, Ghost module in place of partial deep convolution module is used to improve model running speed. Then SIoU, instead of the original localization loss function, is performed to optimize model training process. Finally, the proposed improved YOLOv7 model is applied to different road defect datasets and compared with other methods, such as Faster R-CNN, CenterNet, DETR, YOLOv6 and the original YOLOv7 model. The results show that the proposed method has ubiquitous advantages than the above-mentioned methods, with the average mAP, F1 value and FPS value of 85.8%, 0.697 and 62.13fps respectively. Furthermore, the values of the parameters Params and FLOPs also decrease to some degree.

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Section: Introductionmentioning

confidence: 99%