电源缺陷检测与改进的YOLOv12和ROI 伪点云视觉分析
1School of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了用于电力设备故障检测的先进YOLOv12框架,提高了复杂环境中的准确性. 该系统生成3D伪点云,用于直观的缺陷可视化和分析.
科学领域:
- 电气工程 电气工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 电力设备检查面临的挑战是微妙的缺陷和混乱的背景.
- 准确的故障检测对于电网可靠性和维护至关重要.
研究的目的:
- 开发一个改进的深度学习框架,用于多类电源缺陷检测.
- 为了提高复杂的检查场景的稳定性和准确性.
- 为缺陷结构检查提供直观的视觉分析.
主要方法:
- 一个改进的YOLOv12框架,包括一个先导区域注意 (PG-RA) 模块和一个轻量级残余高效层聚合网络 (LR-RELAN).
- 一个双频谱自适应融合损失 (DSAF损失) 函数,用于改进分类和回归.
- 兴趣区域 (ROI) 伪点云的构建和无声化策略 (SOR,ROR) 的比较.
主要成果:
- 拟议的框架表明,在复杂场景中检测准确性和稳定性得到了提高.
- 实时性能得到维护.
- ROI伪点云为实际的缺陷结构检查提供了直观的辅助视图.
结论:
- 增强的YOLOv12框架有效地解决了电力设备故障检测方面的挑战.
- 集成PG-RA,LR-RELAN和DSAF损失导致了卓越的性能.
- ROI伪点云模块增强了系统的可解释性,并有助于实际的缺陷分析.
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