Research and Implementation of Road Damage Detection Algorithm Based on Object Detection Network

2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT) Pub Date : 2023-06-16 DOI:10.1109/AINIT59027.2023.10212930

Zhuohui Chen, Dahao Wang, Yezhe Wang, Shun-Ping Lin, Haoran Jia, Peixin Lin, Yixian Liu, Ling Chen

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Abstract

Various types of road damage occur frequently, which can affect the smooth running of vehicles. The detection of road surface damage is of great significance for road surface maintenance and smooth traffic flow. First, this paper makes descriptive statistics on RDD2020 dataset, and deals with the mislabeled categories in the dataset, through which 14,569 samples are obtained. A single-stage object detection network YOLOv5 is then constructed to detect road damage on the RDD2020 dataset. The experiment results show that the proposed network is effective in road damage detection of RDD2020 dataset. Faced with high-cost detection methods, a convenient and efficient road damage detection network is urgently needed. In this paper, a road damage detection system is deployed, which can detect the location of road damage and identify the types of road damage in real-time under the camera shooting.

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基于目标检测网络的道路损伤检测算法研究与实现

各种类型的道路损坏频繁发生，影响车辆的平稳行驶。路面损伤检测对于路面养护和交通畅通具有重要意义。首先，本文对RDD2020数据集进行描述性统计，对数据集中的误标注类别进行处理，得到14569个样本。然后构建单级目标检测网络YOLOv5，在RDD2020数据集上检测道路损伤。实验结果表明，该网络对RDD2020数据集的道路损伤检测是有效的。面对高成本的检测方法，迫切需要一个方便高效的道路损伤检测网络。本文部署了一种道路损伤检测系统，该系统可以在摄像机拍摄下实时检测道路损伤位置，识别道路损伤类型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2023 4th International Seminar on Artificial Intelligence, Networking and Information Technology (AINIT)

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