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The application of artificial intelligence has greatly enhanced object detection capabilities across various domains, providing efficient solutions to real-world challenges. However, one area that has received limited attention is the detection of objects on solar panels and developing effective methods for their removal. This research paper explores the use of the YOLOv8 methodology to train an algorithm for identifying such objects on the surface of solar panels. The experiment was conducted in a tropical region during daylight hours. We used the Roboflow Object Detection tool to annotate the dataset and classify multiple objects on the solar panel. We then develop, train, validate, and report a comparative analysis to assess the performance metric using the Yolo-based models.

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YOLO-Based Models for Detecting Objects on the Surface of Solar Panels in Tropical Regions

  • 会议论文

  • 发表日期：

    2025年

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  • Bisheshwar Dev Sharma,
  • Dipta Banik,
  • Paramartha Banerjee,
  • Progyajyoti Paul,
  • Chiradeep Mukherjee,
  • Sudipta Basu Pal

摘要

The application of artificial intelligence has greatly enhanced object detection capabilities across various domains, providing efficient solutions to real-world challenges. However, one area that has received limited attention is the detection of objects on solar panels and developing effective methods for their removal. This research paper explores the use of the YOLOv8 methodology to train an algorithm for identifying such objects on the surface of solar panels. The experiment was conducted in a tropical region during daylight hours. We used the Roboflow Object Detection tool to annotate the dataset and classify multiple objects on the solar panel. We then develop, train, validate, and report a comparative analysis to assess the performance metric using the Yolo-based models.