| 李泉成,曹江涛,姬晓飞.光流增强的红外成像气体泄漏检测方法[J].电子测量与仪器学报,2023,37(3):50-56 |
| 光流增强的红外成像气体泄漏检测方法 |
| Infrared imaging gas leak detection method with optical flow enhancement |
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| DOI： |
| 中文关键词: 光流法 气体泄漏检测 红外成像 计算机视觉 |
| 英文关键词:optical flow gas leak detection infrared imaging computer vision |
| 基金项目:国家自然科学基金(61673199)项目资助 |
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| 中文摘要: |
| 危险气体检测技术是石化等企业安全生产的必要保障之一,在工业生产中有着重要的作用。 为了弥补现阶段基于红外
成像技术的气体泄漏检测算法单帧检测的不足,提高检测精度,本文提出了光流增强的红外成像气体泄漏检测方法。 首先使用
光流网络提取视频中的运动特征,然后将运动特征与原图融合生成光流增强的气体泄漏图像送入 YOLO 网络进行检测,最终确
定视频中是否存在气体泄漏并获取其位置信息。 经过同增强前数据的对比测试,该方法将召回率保持在可接受范围内的同时,
虚警率由 17. 87%降低至 0. 60%、精确率由 77. 21%提升至 99. 99%;检测速度约为 13 fps,可实现实时检测。 |
| 英文摘要: |
| Hazardous gas detection technology is one of the necessary guarantees of safety in many industries such as petrochemical
enterprises, and plays an important role in industrial production. To make up for the shortage of single-frame gas leak detection algorithm
which based on infrared imaging technology and improve the detection accuracy, a method of optical flow enhanced infrared imaging gas
leak detection was proposed in this paper. The motion features in the video are extracted with the optical flow network in the first step.
Then the motion features are fused with the original image to generate an optical flow enhanced gas leak image to be sent to the YOLO
network for detection. Finally, whether there is a leakage in the video was determined and its location was obtained. After a comparison
with the data before enhancement, the method keeps the recall rate in an acceptable range while the false alarm rate is reduced from
17. 87% to 0. 60%, and precision is improved from 77. 21% to 99. 99%. The detection speed is about 13 fps, which satisfies the needs
of real-time detection. |
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