An Improved YOLOv5 Crack Detection Method Combined With Transformer
变压器
计算机科学
工程类
电气工程
电压
作者
Xuezhi Xiang,Zhiyuan Wang,Yulong Qiao
出处
期刊:IEEE Sensors Journal [Institute of Electrical and Electronics Engineers] 日期:2022-07-15卷期号:22 (14): 14328-14335被引量:23
标识
DOI:10.1109/jsen.2022.3181003
摘要
Efficient detection of pavement cracks can effectively prevent traffic accidents and reduce pavement maintenance costs. In order to overcome the complicated and uneconomical disadvantages of traditional crack detection methods, this paper introduces a pavement crack detection network based on deep learning, which can automatically detect pavement cracks and achieves excellent detection accuracy. And the network can easily use the sensors to collect data to facilitate industrial applications. In additional, considering that most cracks have slim feature, we apply the latest Transformer module in the network to improve the effect of cracks detection. Transformer has a strong ability to capture the long-range dependence of the cracks, which enables the network to learn the context information of the crack region. Furthermore, the network also utilizes some techniques to improve the ability of algorithm to detect various cracks. Our network is trained on pavement data sets containing India, the Czech Republic and Japan. It achieved F1 scores of 0.6739 and 0.6650 on two online test sets with fewer network parameters.