使用新的DCD-YOLOv8s模型改进光伏缺陷检测的有效方法
Mohammed Al-Mahbashi1,2, Sara Abdelwahab Ghorashi3, Abdolraheem Khader4
1School of Electronic and Control Engineering, Chang'an University, Xi'an, 710064, Shaanxi, People's Republic of China.
Scientific reports
|November 3, 2025
概括
一个新的DCD-YOLOv8s算法通过使用先进的模块和自定义数据集来改进光伏 (PV) 面板缺陷检测. 这提高了可靠可再生能源系统维护的效率和准确性.
科学领域:
- 可再生能源系统可再生能源系统
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 光伏 (PV) 系统对于可再生能源至关重要,但表面缺陷会降低效率.
- 目前的检查方法效率低下,深度学习模型缺乏适应性和性能.
研究的目的:
- 开发一个增强的YOLOv8算法,用于准确和高效的光伏表面缺陷检测.
- 为了提高特征提取,本地化和检测准确度,同时最大限度地降低计算负载.
主要方法:
- 拟议的DDC-YOLOv8s算法集成可变形卷积网络 (DCNv3),协调注意力 (CA) 和动态头部 (DyHead) 模块.
- 创建了一个自定义数据集,结合了公共和现场收集的光伏面板缺陷图像.
- 利用数据增强和自我训练策略来扩展数据集.
主要成果:
- DCD-YOLOv8s的F1得分为92.8%,mAP@50得分为95.0%,mAP@50-95得分为82.3%.
- 与多个现有的YOLO和RT-DETR模型相比,表现出卓越的性能.
- 保持了 45.9 FPS 的高推断速度.
结论:
- DCD-YOLOv8s显示了基于无人机的实时检查系统的巨大潜力.
- 该算法有助于成本效益高,可靠的光伏系统维护.
- 进一步开发可以实现直接部署无人机,以提高运营效率.
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