Content-Aware Explainable Pavement Distress Detection Model

To address the challenges of using high-resolution pavement images as input for existing convolutional neural network models and the inability of existing preprocessing algorithms to effectively perceive and retain information from low-ratio distress regions in original pavement images, a novel architectural unit called adaptive perception module (APM) paying greater attention to pavement distress region is proposed with the help of visual interpretation techniques, which achieves a rapid and accurate detection of pavement distress in high-resolution images and could be used to build a software system for automatic detection of pavement distress based on computer vision. Firstly, big kernel convolution and residual operations are used to reduce the origin image resolution and get the low-level but rich feature representation. Secondly, attention mechanism is developed to perceive and activate the region of pavement distress and filter the irrelevant background pixel noise. By means of joint learning, APM training could be completed without additional cost. After the visual interpretation method is used to aid the selection and design of the specific structure of APM, experimental results on the latest public dataset CQU-BPMDD show that the proposed APM significantly improves the classification accuracy, up to 84.47%. Experiments across different datasets CQU-BPDD demonstrate the generalization and robustness of APM. Code is available on https://github.com/Li-Ao-Git/apm.