基于DHLNet的光伏电池板上的异物检测
Haibo Jin1, Mengjiao Li2, Xiaoyun Lv2
1Faculty of Software, Liaoning Technical University, Huludao, 125105, Liaoning, China. jinhaibo@lntu.edu.cn.
Scientific reports
|February 9, 2026
概括
本研究介绍了DHLNet,这是一种用于检测光伏电池板上的外来物体的先进模型. DHLNet提高了检测准确度,并支持实时监控,这对于光伏发电厂的安全性和效率至关重要.
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:
- 在光伏 (PV) 面板上的异物积累会降低性能,并可能造成损坏.
- 检测的挑战包括低对比度,复杂的物体形状和不平衡的数据集.
- 自动检测对于光伏发电厂的安全和经济运行至关重要.
研究的目的:
- 提出一种有效和高效的模型,用于在光伏电池板上自动检测异物.
- 解决现有方法在复杂背景中检测小物体方面的局限性.
- 为了提高光伏系统外来物体识别的准确性和可靠性.
主要方法:
- 开发DHLNet模型,采用双流特征金字塔网络来改进小物体检测.
- 集成高频增强模块以提高对模糊边缘的灵敏度.
- 整合大型可分离内核注意力机制,以增强特征提取.
主要成果:
- DHLNet实现了79.9%的mAP@0.5和58.8%的mAP@0.5:0.95,超过了原来的YOLO11n算法.
- 在F1的得分达到0.761,1.3%的改善.
- 该模型展示了实时推断能力,适用于现场和在线监控.
结论:
- DHLNet有效地检测光伏电池板上的异物,改进了现有的算法.
- 该模型的轻量结构和增强功能为自动光伏检查提供了实际潜力.
- 通过可靠的异物检测,DHLNet为光伏发电厂的安全和高效运行做出了贡献.
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