通过YOLO-ACF的自适应互补融合来增强光伏面板缺陷检测
Wenwen Pan1, Xiaofei Sun2, Yilun Wang1
1School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, 210014, China.
Scientific reports
|November 3, 2024
概括
我们开发了一个自适应互补融合 (ACF) 模块,用于使用电光发光图像检测光伏面板缺陷. 这种方法提高了准确性和速度,同时减少了模型大小,提高了太阳能电池板质量控制.
科学领域:
- 材料科学 材料科学 材料科学
- 电气工程 电气工程
- 计算机视觉 计算机视觉
背景情况:
- 使用电光发光 (EL) 图像检测光伏 (PV) 面板中的缺陷对于质量控制至关重要.
- 挑战包括错误检测和错误报警,因为缺陷和复杂的背景之间具有相似的特征.
研究的目的:
- 提出一个自适应互补融合 (ACF) 模块,以提高光伏面板缺陷检测.
- 为了提高检测性能,减少模型大小,加快检测速度.
主要方法:
- 将自适应互补融合 (ACF) 模块集成到YOLOv5对象检测框架中.
- 培训和验证使用光伏电池板的4500个电解发光图像数据集.
主要成果:
- 与YOLOv8.8.5相比,YOLO-ACF在回忆中显示了5.2%的改善,在mAP50中显示了0.8%的改善,在mAP50-95中显示了2.3%的改善.
- 与基线YOLOv5.5相比,YOLO-ACF实现了12.9%的参数减少,12.4%的重量减少和4.2%的时间减少,FPS增加了5%,与基线YOLOv5.5相比.
结论:
- 拟议的YOLO-ACF方法有效地平衡了检测性能,模型复杂性和光伏电池板缺陷检测速度.
- ACF模块在各种缺陷类型中显示了多功能性,并增强了太阳能电池板的整体质量控制过程.
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