用动态多核学习进行红外气体泄漏细分的YOLOv8-Seg:一种弱监督的方法
Haoyang Shen1, Lushuai Xu2,3, Mingyue Wang4
1College of Carbon Neutral Energy, China University of Petroleum, Beijing 102249, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
这项研究通过使用改进的YOLOv8-seg网络和框注释来加强石油和天然气设施的气体泄漏检测. 这种新方法显著提高了红外气体泄漏成像的细分精度.
科学领域:
- 工业安全
- 计算机视觉
- 红外成像
背景情况:
- 气体泄漏检测对于石油和天然气设施的安全至关重要.
- 红外成像提供实时,非接触式监控,但面临细分挑战.
- 现有的像素级网络在准确性,粗的边缘和的边界方面扎.
研究的目的:
- 开发一个新的像素级分段网络培训框架,以加强气体检测.
- 提高YOLOv8-seg网络在红外气体泄漏应用中的分段性能.
- 为培训细分网络引入低成本,低监督的学习方法.
主要方法:
- 使用视觉背景提取器 (ViBe) 与YOLOv8-det生成训练口罩引入了一个动态值.
- 设计了一个具有动态内核,多分支协作,可变形卷积和注意力机制的新细分头.
- 实施了由ViBe-CRF信心加权的联合Dice-BCE损失来精炼气边缘.
主要成果:
- 在F1得分上有6.4%的增长,在欧盟的平均交叉点上有7.6%的改善.
- 显著减少气体边缘的粗和凸,提高分段精度.
- 展示了一种新的,高效的实时检测算法,用于红外气体泄漏成像.
结论:
- 拟议的框架有效地增强了YOLOv8-seg在石油和天然气设施中的气体泄漏检测.
- 该方法为实时红外气体泄漏监测提供了更准确和更强大的解决方案.
- 引入了一种有价值的低监督学习策略,以成本高效地培养像素级细分网络.
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