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Exploring the Tricks for Road Damage Detection with A One-Stage Detector

机译：用一级探测器探索道路损伤检测的技巧

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摘要

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.
机译：快速准确的道路损伤检测对于道路检查自动化至关重要。本文介绍了我们的解决方案，提交了2020年IEEE国际大数据会议的全球道路损伤检测挑战，基于深度学习的数字图像中的典型道路损伤检测。最近提出的yolov4被选为基线网络，而数据增强，转移学习，优化锚点的影响是评估的。我们提出了一种基于生成的对冲网络的新型道路损伤数据生成方法，可以使用单一模型生成多级样本。评估结果证明了不同技巧及其在道路损伤检测任务上的组合的有效性，为实际应用提供了参考。我们的解决方案代码可在https://github.com/zhangxg001/raddambedetection.git上获得。

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