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To address the challenges of high missed detection rates, complex backgrounds, unclear defect features, and uneven difficulty levels in target detection during the industrial process of photovoltaic panel defect detection, this article proposes an infrared detection method. . To address the challenges of high missed detection rates, complex backgrounds, unclear defect features, and uneven difficulty levels in target detection during the industrial process of photovoltaic panel defect detection, this article proposes an infrared detection method. . Infrared (IR) anomaly detection has become a powerful tool for spotting issues like diode failures, hotspots, electrical isolation problems, and string outages. At the same time large solar power plants. . Abstract—Utility-scale solar arrays require specialized inspection methods for detecting faulty panels. Photovoltaic (PV) panel faults caused by weather, ground leakage, circuit issues, temperature, environment, age, and other damage can take many forms but often symptomatically exhibit temperature. . This powerful diagnostic tool can detect hotspots and other potential problems that could impair the performance of solar panels. By utilizing a large-scale IR image dataset obtained from real solar fields, the proposed CNN model is designed to effectively detect and classify various faults. .
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