YOLOv5-PD: A Model for Common Asphalt Pavement Defects Detection

Abstract

In asphalt pavement detection, the defect scale changes greatly, mainly including mesh cracks, patches, and potholes. In the case of large scale, the texture feature is not clear, and the information is easily lost in the feature extraction process. Correspondingly, the number of small-scale holes is often very large, which also puts forward higher requirements for the detection model. In view of the above problems, this paper proposed a model for common asphalt pavement defects detection called YOLOv5-PD. In order to reduce the loss of information and expand the receptive field of the model, Big Kernel convolution was used to replace a part of the convolution in the original CSPDarknet. The texture feature information of the cracks is retained to the greatest extent. In order to enhance the detection performance of small defects, convolution channel attention mechanism was added after each feature fusion layer, and performs attention processing on the feature map after concat to find the defect location. This study used a public pavement defect dataset from Brazil. In this work, ablation experiments were carried out according to the task scenario, and the improved effects were compared and analyzed. The proposed model is compared with other versions of models and advanced models, which proves the superiority of the proposed model. The mAP of proposed model reached 73.3% and the model inference speed reached 41FPS, which can meet real time engineering application requirements.