Abstract: To investigate the influence of wet environment on automatic crack detection of asphalt pavement,through YOLOv5 target detection algorithm that based on the principle of deep learning,an asphalt pavement crack detection model is established.Based on the model,a comparison experiment of crack detection under wet and dry conditions is set up,the accuracy and confidence of crack detection results of asphalt pavement under the two conditions are compared.The research results show that the wet environment expands the identification features of pavement cracks in the deep learning network,improves the effect of pavement crack detection.The accuracy of crack identification on dry pavement is 80.70%,the accuracy of crack detection on wet pavement is 89.47%,the accuracy of crack detection model on asphalt pavement under wet conditions is improved by 8.77%.At the same time,It is found that the average value of confidence is 0.72 in dry environment and 0.78 in wet environment,and there is a significant positive correlation between wetting and the confidence of crack detection.The research results provide a new idea for the improvement of automatic crack detection of asphalt pavement and an effective tool for pavement maintenance management.

Key words: Intelligent transportation, Crack detection, Deep learning, Object detection, YOLOv5, Wetting conditions