Pavement Crack Detection Method of Street View Images Based on Deep Learning
Zekai Shu, Zhaoyu Yan, Xihang Xu
Abstract
Abstract Pavement crack detection is a challenging task for carrying out pavement maintenance works. Deep learning method is regarded as an effective and accurate way to detect pavement cracks. However, this often requires a large dataset composed of different crack images. This paper introduces a convenient and low-cost method to collect pavement images by using street view images. 400 images from 5 cities are collected and labeled to form the dataset. Then, it is applied to train a target detection network YOLOv5, which is the latest version of YOLO network. The result shows that this network can effectively detect crack with mAP of over 70% and detection time of 152ms, which are all better than another classical method YOLOv3. Considering the easiness of collecting images, this method can be a suitable way to evaluate the pavements.