ORIGINAL RESEARCH article
Front. Phys.
Sec. Optics and Photonics
Volume 13 - 2025 | doi: 10.3389/fphy.2025.1603047
High-Accuracy Combustible Gas Cloud Imaging System Using YOLO-Plume Classification Network
Provisionally accepted- 1Jilin University, Changchun, China
- 2Sinotest Equipment Co., Ltd., Changchun, China
- 3Jingwei Hirain Co., Ltd., Changchun, China
- 4Changchun UP Optotech Co., Ltd., Changchun, China
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Effective natural gas leakage detection is of great significance in terms of economy, environment and safety. Due to the irregular shape and ambiguous boundary of the gas, traditional motion detection algorithms are difficult to adapt to the changes in the gas movement state with the environment, resulting in an increased probability of false alarms. To address this issue, this paper proposes a gas plume-constrained YOLOv11 model based on infrared imaging detection technology, named YPCN (YOLO-Plume Classification Network). A new backbone feature extraction network, MobileNetV4, is selected to replace the original backbone network, and SPD-Conv is introduced in the segmentation head network. This network effectively reduces model complexity and enhances inference speed while maintaining detection accuracy. Additionally, a gas plume model is introduced as a key physical constraint condition in the loss function to enhance the model's accuracy, segmentation precision, and generalization ability in handling gas plume tasks. Moreover, this paper constructs a gas leakage dataset consisting of 13109 frames, covering different distances, sizes, and backgrounds. Experimental results show that the proposed model achieves an F1-score of 88.97% and an IoU of 89.74%, improving upon the baseline by 7.37% and 7.59%, respectively, with a detection accuracy reaching 99.78%.
Keywords: Natural gas leakage, Mid-infrared spectrum, combustible gas cloud imaging, plume classification network, YOLO
Received: 31 Mar 2025; Accepted: 04 Jun 2025.
Copyright: © 2025 Zhou, Liu, Zhang, Hu, Leng, Chen and Sun. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Chen Chen, Jilin University, Changchun, China
Feng Sun, Jilin University, Changchun, China
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