Xu Shiwei, Wang Xia, Sun Qiyang, Dong Kangjun
Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel). 2024 Jul 4;24(13):4345. doi: 10.3390/s24134345.
The integration of visual algorithms with infrared imaging technology has become an effective tool for industrial gas leak detection. However, existing research has mostly focused on simple scenarios where a gas plume is clearly visible, with limited studies on detecting gas in complex scenes where target contours are blurred and contrast is low. This paper uses a cooled mid-wave infrared (MWIR) system to provide high sensitivity and fast response imaging and proposes the MWIRGas-YOLO network for detecting gas leaks in mid-wave infrared imaging. This network effectively detects low-contrast gas leakage and segments the gas plume within the scene. In MWIRGas-YOLO, it utilizes the global attention mechanism (GAM) to fully focus on gas plume targets during feature fusion, adds a small target detection layer to enhance information on small-sized targets, and employs transfer learning of similar features from visible light smoke to provide the model with prior knowledge of infrared gas features. Using a cooled mid-wave infrared imager to collect gas leak images, the experimental results show that the proposed algorithm significantly improves the performance over the original model. The segment mean average precision reached 96.1% (mAP50) and 47.6% (mAP50:95), respectively, outperforming the other mainstream algorithms. This can provide an effective reference for research on infrared imaging for gas leak detection.
视觉算法与红外成像技术的融合已成为工业气体泄漏检测的有效工具。然而，现有研究大多集中在气体羽流清晰可见的简单场景，对于目标轮廓模糊、对比度低的复杂场景中的气体检测研究较少。本文使用制冷中波红外（MWIR）系统提供高灵敏度和快速响应成像，并提出了用于中波红外成像中气体泄漏检测的MWIRGas-YOLO网络。该网络能有效检测低对比度的气体泄漏并分割场景中的气体羽流。在MWIRGas-YOLO中，它利用全局注意力机制（GAM）在特征融合过程中充分聚焦于气体羽流目标，添加小目标检测层以增强小尺寸目标的信息，并采用可见光烟雾相似特征的迁移学习为模型提供红外气体特征的先验知识。使用制冷中波红外成像仪收集气体泄漏图像，实验结果表明，所提算法相比原模型性能有显著提升。分割平均精度分别达到96.1%（mAP50）和47.6%（mAP50:95），优于其他主流算法。这可为气体泄漏检测的红外成像研究提供有效参考。
