复杂背景绝缘体的实例细分方法基于改进的SOLOv2
Ze Chen1, Yangpeng Ji1, Xiaodong Du1
1State Grid Hebei Electric Power Research Institute, Shijiazhuang 050000, China.
Sensors (Basel, Switzerland)
|September 13, 2025
概括
本研究引入了一种增强的SOLOv2实例细分模型,用于在无人机 (UAV) 图像中精确检测绝缘体轮,改进电网检查中的缺陷分析.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 电气工程 电气工程
背景情况:
- 准确的绝缘体轮划分对于基于无人机 (UAV) 的输电线路检查中缺陷分析至关重要.
- 现有的方法难以处理复杂的图像背景和精确的边缘检测.
研究的目的:
- 开发一个先进的实例细分框架,用于在无人机图像中准确地检测绝缘体轮.
- 提高输电线路检查中缺陷分析的效率和准确性.
主要方法:
- 一个增强的SOLOv2实例细分模型,包含一个非亚样本轮转换 (NSCT) 预处理的边缘通道.
- 利用HRNet作为脊柱,调整道数量和增加层次,用于多规模的特征提取.
- 集成了一个卷积块注意模块 (CBAM),用于改进面具质量和对象检测.
- 使用虚幻引擎 (UE4) 生成虚拟数据集,以增强模型的稳定性并减少注释工作.
主要成果:
- 在具有AP0.50 (90.21%),AP0.75 (83.34%) 和AP[0.50:0.95] (67.26%) 的电网图像测试集上取得了卓越的性能.
- 在复杂的传输线图像中精确捕获绝缘体边缘的增强能力.
- 该框架在准确性和精确性方面超越了现有的方法.
结论:
- 拟议的增强SOLOv2框架通过允许精确的绝缘体轮划分,显著提升智能输电线路检查.
- 这种方法有助于更准确和更有效的后续缺陷分析,有助于改进电网维护.
- 集成NSCT,HRNet,CBAM和UE4生成的数据为现实世界检查挑战提供了强大的解决方案.
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