@inproceedings{56338877f87241e7ae00147a2b30eceb,
title = "Deep Learning Method for Leakage Location Detection of Pneumatic Systems Based on Infrared Thermal Image Evaluation",
abstract = "Pneumatic systems are an essential fluid transmission method in the industrial field, which can achieve the transmission and control of power or signals. Leakage in pneumatic systems is challenging to detect and is a highly harmful fault. Most existing leak detection methods use flow or pressure sensors to detect specific leakage amounts, while leak localization is still in the traditional manual detection, which significantly restricts localization efficiency. This article establishes a deep learning framework-based thermal image localization method for pneumatic system leak detection. Infrared images captured in the pneumatic system were collected, and then a deep-learning localization method based on the YOLO framework was established. Then, the recognition accuracy of this method was calculated. The results indicate that the accuracy has reached 99.5\% of mAP\_0.5 and 86.27\% of mAP\_0.5:0.95, indicating that this work is a meaningful study that can apply intelligent computing to engineering.",
keywords = "Deep neural network, Leakage location, Object detection, Pneumatic system",
author = "Jiaqi Chang and Yan Shi and Liman Yang and Yanxia Niu and Yulong Nie and Zhiguo Yang and Lei Li and Wenchao Zhang",
note = "Publisher Copyright: {\textcopyright} 2024 ACM.; 7th International Conference on Software Engineering and Information Management, ICSIM 2024 ; Conference date: 23-01-2024 Through 25-01-2024",
year = "2024",
month = jan,
day = "23",
doi = "10.1145/3647722.3647734",
language = "英语",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery ",
pages = "77--83",
booktitle = "Proceedings of the 2024 7th International Conference on Software Engineering and Information Management, ICSIM 2024",
address = "美国",
}