文献类型：期刊文献

英文题名：TSFF-Net: A novel lightweight network for video real-time detection of SF6 gas leaks

作者：Yao, Jianjiang[1];Xiong, Zhanhang[1];Li, Shugang[2];Yu, Zhaoxu[1];Liu, Yalei[1]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai, Peoples R China;[2]Shanghai Univ, Dept Informat Management, Shanghai, Peoples R China

年份：2024

卷号：247

外文期刊名：EXPERT SYSTEMS WITH APPLICATIONS

收录：;EI(收录号：20240515460188);WOS:【SCI-EXPANDED(收录号:WOS:001171121500001)】；

基金：This work was supported by the Natural Science Foundation of China under grants 71871135 and 72271155.

语种：英文

外文关键词：Infrared video image; SF6 gas leak detection; TSFF-Net; Attention feature fusion; Motion features

摘要：Detecting Sulphur hexafluoride (SF6) gas leaks using infrared video images is the latest technology. However, the current algorithm for autonomous real -time detection is imprecise and incapable of performing the real -time monitoring task effectively. In response to the aforementioned issues, we present a temporal- spatial feature fusion network (TSFF-Net) specifically designed for video real -time detection of SF6 gas leaks. Compared with the existing SF6 gas leak detection model based on deep learning, the biggest feature of TSFF-Net is the introduction of foreground pixel images with SF6 gas movement characteristics, which greatly enhances the model's ability to detect SF6 gas leaks. Simultaneously, by designing a new attention feature fusion module, introducing the Neck module of motion features, and the Backbone module to enhance small target feature extraction, we achieve high-precision SF6 gas leak detection in various complex scenarios, such as sparse leaks, complex backgrounds, camera shake, etc. The mean average precision (mAP) of TSFF-Net in the validation set was 56.3%, which is 13.2% higher than that of Yolov8s. In addition, the model proposed in this paper has parameters that are only 2.6 M, making it suitable for deployment on mobile or embedded devices. The code and test results are at https://github.com/1jianyao/TSFF-Net/tree/main.