@inproceedings{6c32412ae07f4185a944436f4fe6ffc0,
title = "An Improved Lightweight YOLOv5 Network for Defect Detection of Rail Fasteners",
abstract = "The rail fastener is an important infrastructure of railway line system to ensure the safety of railway operation. There is an urgent need for a set of automatic defect inspection scheme for rail fasteners which have high efficiency and accuracy. To this end, this paper proposes a modified lightweight YOLOv5 model considering the application scenario of UAV. We reconstruct the backbone on the basis of ShuffleNetV2 and RepVGG, and switch the detector head to decoupled type from YOLOX. Data augmentation is adopted to address the problem of deficient defect samples. The results show that Yoloxs-lite-s model with ShuffleNetV2 backbone and YOLOX-s head is the most ideal model.",
keywords = "deep learning, defect inspection, object detection, rail fastener",
author = "Zhen Dai and Zhipeng Wang and Limin Jia and Yong Qin and Lei Tong and Jing Cui",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 Global Reliability and Prognostics and Health Management Conference, PHM-Yantai 2022 ; Conference date: 13-10-2022 Through 16-10-2022",
year = "2022",
doi = "10.1109/PHM-Yantai55411.2022.9941763",
language = "英语",
series = "2022 Global Reliability and Prognostics and Health Management Conference, PHM-Yantai 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Wei Guo and Steven Li",
booktitle = "2022 Global Reliability and Prognostics and Health Management Conference, PHM-Yantai 2022",
address = "美国",
}