Exploring the Tricks for Road Damage Detection with A One-Stage Detector
Xiaoguang Zhang, Xuan Xia, Nan Li, Lin Ma, Junlin Song, Ning Ding
Abstract
Fast and accurate road damage detection is essential for the automatization of road inspection. This paper describes our solution submitted to the Global Road Damage Detection Challenge of the 2020 IEEE International Conference on Big Data, for typical road damage detection in digital images based on deep learning. The recently proposed YOLOv4 is chosen as the baseline network, while the effects of data augmentation, transfer learning, Optimized Anchors, and their combination are evaluated. We propose a novel road damage data generation method based on a generative adversarial network, which can generate multi-class samples with a single model. The evaluation results demonstrate the effectiveness of different tricks and their combinations on the road damage detection task, which provides a reference for practical application. The code of our solution is available at https://github.com/ZhangXG001/RoadDamgeDetection.git.