Liu Yonghao, Duan Minglei, Ding Guangen, Ding Hongwei, Hu Peng, Zhao Hongzhi
School of Information, Yunnan University, Kunming 650500, China.
Yunnan Province Highway Networking Charge Management Co., Kunming 650000, China.
Entropy (Basel). 2023 Aug 31;25(9):1280. doi: 10.3390/e25091280.
In recent years, the number of traffic accidents caused by road defects has increased dramatically all over the world, and the repair and prevention of road defects is an urgent task. Researchers in different countries have proposed many models to deal with this task, but most of them are either highly accurate and slow in detection, or the accuracy is low and the detection speed is high. The accuracy and speed have achieved good results, but the generalization of the model to other datasets is poor. Given this, this paper takes YOLOv5s as a benchmark model and proposes an optimization model to solve the problem of road defect detection. First, we significantly reduce the parameters of the model by pruning the model and removing unimportant modules, propose an improved Spatial Pyramid Pooling-Fast (SPPF) module to improve the feature signature fusion ability, and finally add an attention module to focus on the key information. The activation function, sampling method, and other strategies were also replaced in this study. The test results on the Global Road Damage Detection Challenge (GRDDC) dataset show that the FPS of our proposed model is not only faster than the baseline model but also improves the MAP by 2.08%, and the size of this model is also reduced by 6.07 M.
近年来，世界各地因道路缺陷导致的交通事故数量急剧增加，道路缺陷的修复和预防成为一项紧迫任务。不同国家的研究人员提出了许多模型来处理这项任务，但其中大多数要么检测精度高但速度慢，要么精度低但检测速度快。虽然在精度和速度方面取得了不错的成果，但模型对其他数据集的泛化能力较差。鉴于此，本文以YOLOv5s作为基准模型，提出了一种优化模型来解决道路缺陷检测问题。首先，我们通过剪枝模型和去除不重要的模块显著减少了模型参数，提出了一种改进的空间金字塔池化快速（SPPF）模块来提高特征签名融合能力，最后添加了一个注意力模块来聚焦关键信息。本研究还对激活函数、采样方法等策略进行了替换。在全球道路损伤检测挑战赛（GRDDC）数据集上的测试结果表明，我们提出的模型不仅帧率比基线模型更快，而且平均精度均值（MAP）提高了2.08%，该模型的大小也减少了6.07兆字节。
