优化了基于YOLO的模型,用于在电光发光图像中检测光伏缺陷
Achit Mohamed1, Yassa Nacera1, Bouzida Ahcene1
1Laboratoire des Matériaux et Développement Durable (LMDD), University of Bouira, Bouira, 10000, Algeria.
Scientific reports
|September 26, 2025
概括
一个新的深度学习模型,PV-YOLOv12n,通过使用电解发光 (EL) 图像来改善光伏 (PV) 面板缺陷检测. 这种增强的模型准确地识别了裂和位移等关键缺陷,确保了太阳能系统的可靠性.
科学领域:
- 可再生能源系统可再生能源系统
- 在工程领域的人工智能.
- 材料科学 材料科学 材料科学
背景情况:
- 光伏 (PV) 系统的可靠性对于可持续的能源生产至关重要.
- 在光伏电池板中自动检测缺陷对于维护和性能至关重要.
- 深度学习对象检测模型为光伏缺陷识别提供了有前途的解决方案.
研究的目的:
- 推出PV-YOLOv12n,这是一个优化的YOLOv12n变体,用于检测光伏面板电解发光 (EL) 图像中的缺陷.
- 增强特征提取,以更好地检测关键缺陷,如大裂,位移和材料不一致.
- 评估PV-YOLOv12n的性能和效率与基准数据集上的现有模型相比.
主要方法:
- 通过将P5规模的A2C2f模块集成到YOLOv12n架构中来开发PV-YOLOv12n.
- 使用电光发光 (EL) 图像进行缺陷检测.
- 使用PVEL-AD和Roboflow数据集进行实验验证.
主要成果:
- 在PVEL-AD和Roboflow数据集中,PV-YOLOv12n在PVEL-AD和Roboflow数据集中实现了0.91的平均平均精度 (mAP@50).
- 性能优于基线YOLOv12n,显示了对关键光伏缺陷的改进精度和回忆.
- 展示了增强的概括,mAP@50-95得分为0.58 (PVEL-AD) 和0.75 (Roboflow).
- 保持了高效的推理速度 (4.24 ms 和 4.43 ms),适合实时应用.
结论:
- PV-YOLOv12n有效检测光伏电池板的关键缺陷,提高可靠性.
- 优化的模型支持高效,大规模的太阳能农场检查.
- 集成A2C2f模块显著提高了缺陷检测能力.
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