A Transformer-Optimized Deep Learning Network for Road Damage Detection and Tracking

Author:

Wang Niannian1,Shang Lihang1ORCID,Song Xiaotian2

Affiliation:

1. School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China

2. School of Engineering and Technology, China University of Geosciences (Beijing), Beijing 100083, China

Abstract

To solve the problems of low accuracy and false counts of existing models in road damage object detection and tracking, in this paper, we propose Road-TransTrack, a tracking model based on transformer optimization. First, using the classification network based on YOLOv5, the collected road damage images are classified into two categories, potholes and cracks, and made into a road damage dataset. Then, the proposed tracking model is improved with a transformer and a self-attention mechanism. Finally, the trained model is used to detect actual road videos to verify its effectiveness. The proposed tracking network shows a good detection performance with an accuracy of 91.60% and 98.59% for road cracks and potholes, respectively, and an F1 score of 0.9417 and 0.9847. The experimental results show that Road-TransTrack outperforms current conventional convolutional neural networks in terms of the detection accuracy and counting accuracy in road damage object detection and tracking tasks.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Program for Innovative Research Team (in Science and Technology) in University of Henan Province

Program for Science & Technology Innovation Talents in Universities of Henan Province

Postdoctoral Science Foundation of China

Key Scientific Research Projects of Higher Education in Henan Province

Open Fund of Changjiang Institute of Survey, Lanning, Design and Research

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Pothole road detection and identification based on transfer learning;Fourth International Conference on Sensors and Information Technology (ICSI 2024);2024-05-06

2. Advances in Non-Destructive Testing Methods;Materials;2024-01-24

3. Towards Robust Road Quality Detection Using Different Detection Models;IFIP Advances in Information and Communication Technology;2024

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