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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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太阳能电池板表面缺陷和尘埃检测:深度学习方法

Atta Rahman1

  • 1Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia.

Journal of imaging
|September 26, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种使用深度学习进行太阳能电池板缺陷检测的自动化系统,提高了效率并降低了维护成本. 人工智能模型在识别诸如尘埃和物理损伤等问题方面达到95%以上的准确性.

关键词:
沙特2030年愿景是什么意思这就是YOLOv11的意义.计算机视觉 计算机视觉深度学习是一种深度学习.积极主动地进行维护.实时监控实时监控可再生能源可再生能源的能源.太阳能电池板缺陷检测检测器

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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
  • 计算机视觉 计算机视觉

背景情况:

  • 保持太阳能电池板效率对于可持续能源至关重要.
  • 极端环境条件对光伏 (PV) 系统的性能构成挑战.
  • 需要自动检测缺陷,以减少成本和停机时间.

研究的目的:

  • 开发用于光伏表面的自动缺陷检测管道.
  • 识别和分类五种异常类型:非缺陷,尘埃,缺陷,物理损伤和雪.
  • 通过持续监测,提高太阳能系统的可靠性和成本效益.

主要方法:

  • 一个由8973张图像组成的异质数据集被策划和增强.
  • 一个基于YOLOv11的深度学习模型被训练和微调.
  • 该模型被集成到一个交互式仪表板中,用于实时处理和提醒.

主要成果:

  • 在YOLOv11模型中,平均精度 (mAP@0.5) 达到85%.
  • 精度,回忆和F1得分在各种条件下超过95%.
  • 与手动检查和旧型号相比,该系统显示出更高的精度和推断速度.

结论:

  • 自动视觉检查可降低太阳能装置的劳动力成本和运行停机时间.
  • 开发的管道为主动维护和增强光伏系统寿命提供了一个可扩展的解决方案.
  • 这种方法提高了大型太阳能系统的整体可靠性和成本效益.