MWIRGas-YOLO:基于中波红外成像的气体泄漏检测
Shiwei Xu1, Xia Wang1, Qiyang Sun1
1Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
本研究介绍了MWIRGas-YOLO,这是一个先进的网络,用于通过中波红外 (MWIR) 成像来检测工业气体泄漏. 该算法在具有挑战性的条件下表现出色,提高了低对比度气体羽毛的检测精度.
科学领域:
- 工业安全和环境监测
- 红外成像技术是红外成像技术.
- 计算机视觉算法 计算机视觉算法
背景情况:
- 工业气体泄漏检测依赖于视觉算法和红外成像.
- 现有的方法难以处理复杂的场景,包括模糊的轮和低对比度.
- 有限的研究涉及在具有挑战性的,低可见度的工业环境中探测气体.
研究的目的:
- 开发一种有效的方法来检测中波红外 (MWIR) 成像中的气体泄漏,特别是在复杂的场景中.
- 改进低对比度气体羽毛的检测和细分.
- 为了提高红外气体泄漏检测系统的性能.
主要方法:
- 使用冷却的中波红外 (MWIR) 系统进行高灵敏度成像.
- 提出了MWIRGas-YOLO网络,其中包含了功能融合的全球关注机制 (GAM).
- 集成了一个小型目标检测层,并从可见光烟雾数据中转移学习.
主要成果:
- MWIRGas-YOLO网络在检测低对比度气体泄漏和细分气体方面表现出卓越的性能.
- 取得的高分段平均精度为96.1% (mAP50) 和47.6% (mAP50:95).
- 在实验测试中明显优于原始模型和其他主流算法.
结论:
- 拟议的MWIRGas-YOLO算法为使用MWIR成像检测工业气体泄漏提供了显著的进步.
- 该方法在复杂的场景中有效,轮模糊,对比度低.
- 为未来的红外气体泄漏检测系统研究提供了宝贵的参考.
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