AE-YOLO:基于YOLOv11的轻量级改进模型在光伏面板表面智能缺陷检测中的研究和应用
1School of Intelligent Manufacturing, Panzhihua University, Panzhihua 617000, China.
Materials (Basel, Switzerland)
|December 11, 2025
概括
这项研究介绍了AE-YOLO,这是用于光伏面板表面缺陷检测的轻量级模型. 它提高了准确性和效率,这对于可再生能源的安全性和性能至关重要.
科学领域:
- 可再生能源可再生能源是可再生能源.
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
背景情况:
- 表面缺陷检测对于光伏面板的效率和安全至关重要.
- 传统方法在复杂的背景和准确性方面扎.
- 可再生能源的增长需要先进的检查技术.
研究的目的:
- 开发一种轻量级,高效,准确的光伏面板表面缺陷检测模型.
- 改进现有的YOLOv11架构,用于工业应用.
- 为了应对复杂的背景和低对比度缺陷所带来的挑战.
主要方法:
- 拟议的AE-YOLO模型是YOLOv11.1的增强.
- 引入了一个轻量级的动态下方采样模块 (Adown).
- 集成的高效通道注意力 (ECA) 机制用于功能增强.
主要成果:
- 在PVEL-AD数据集上,AE-YOLO实现了90.3%的mAP@0.5.
- 参数数量减少了18.7%,计算负载减少了19%.
- 实现了259.56 FPS的高推理速度.
结论:
- AE-YOLO提供了一种创新的解决方案,用于实时,准确的光伏电池板缺陷检测.
- 在Adown和ECA模块证明了互补的好处.
- 该模型适用于要求高性能和效率的工业场景.
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