Deep Network For Road Damage Detection
Yuming Liu, Xiaoyong Zhang, Bingzhen Zhang, Zhenwu Chen
Abstract
Heavy use by cars and trucks leads to huge damages, so road damage detection is an essential task to road maintenance. Traditional road damage detection has to require a huge amount of manual effort, it is therefore of great interest to propose vision-based systems that can automatically detect the road damages. In this work, we use deep learning models to detect road damages efficiently. Specifically, we apply a segmentation method to detect the road areas and build a road-interest map for the raw images. Then we adopt the state-of-the-art deep objective detection model including Faster-RCNN and YOLOv4 for completing detection. Experiments convey that the proposed model achieves good detection performance on the IEEE Global Road Damage Detection Challenge 2020.