The Application of a Pavement Distress Detection Method Based on FS-Net

Yun Hou, Yuanshuai Dong, Yanhong Zhang, Zuofeng Zhou, Xinlong Tong, Qingquan Wu, Zhenyu Qian and Ran Li
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Yun Hou: China Highway Engineering Consulting Group Company Co., Ltd., Beijing 100089, China
Yuanshuai Dong: China Highway Engineering Consulting Group Company Co., Ltd., Beijing 100089, China
Yanhong Zhang: China Highway Engineering Consulting Group Company Co., Ltd., Beijing 100089, China
Zuofeng Zhou: Xi’an Institute of Optics and Precision Mechanics, CAS Industrial Development Co., Ltd., Xi’an 710000, China
Xinlong Tong: China Highway Engineering Consulting Group Company Co., Ltd., Beijing 100089, China
Qingquan Wu: Key & Core Technology Innovation Institute of The Greater Bay Area, Guangzhou 510530, China
Zhenyu Qian: China Highway Engineering Consulting Group Company Co., Ltd., Beijing 100089, China
Ran Li: Key & Core Technology Innovation Institute of The Greater Bay Area, Guangzhou 510530, China

Sustainability, 2022, vol. 14, issue 5, 1-17

Abstract: In order to solve the problem of difficulties in pavement distress detection in the field of pavement maintenance, a pavement distress detection algorithm based on a new deep learning method is proposed. Firstly, an image data set of pavement distress is constructed, including large-scale image acquisition, expansion and distress labeling; secondly, the FReLU structure is used to replace the leaky ReLU activation function to improve the ability of two-dimensional spatial feature capture; finally, in order to improve the detection ability of this model for long strip pavement distress, the strip pooling method is used to replace the maximum pooling method commonly used in the existing network, and a new method is formed which integrates the FReLU structure and the strip pooling method, named FS-Net in this paper. The results show that the average accuracy of the proposed method is 4.96% and 3.67% higher than that of the faster R-CNN and YOLOv3 networks, respectively. The detection speed of 4 K images can reach about 12 FPS. The accuracy and computational efficiency can meet the actual needs in the field of road detection. A set of lightweight detection equipment for highway pavement was formed in this paper by purchasing hardware, developing software, designing brackets and packaging shells, and the FS-Net was burned into the equipment. The recognition rate of pavement distress is more than 90%, and the measurement error of the crack width is within ±0.5 mm through application testing. The lightweight detection equipment for highway pavement with burning of the pavement distress detection algorithm based on FS-Net can detect pavement conditions quickly and identify the distress and calculate the distress parameters, which provide a large amount of data support for the pavement maintenance department to make maintenance decisions.

Keywords: image processing; deep learning; pavement distress; object detection (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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