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This study proposes a lightweight dual-modal detection scheme, combining visible and infrared images to address three major challenges in photovoltaic panel defect detection, namely
One of the most effective ways to monitor solar panels for early signs of problems is by using thermal imaging. Infrared (IR) anomaly detection
The adoption of a deep learning-based infrared image detection algorithm for PV modules significantly reduces the cost of manual inspection
By detecting variations in the thermal image of a solar panel, these handheld tools can be used to identify hotspots caused by damage and degradation, allowing for targeted maintenance efforts.
To address these limitations (Hussain & Khanam, 2024), this study proposes a PV panel defect detection method based on YOLOv8 and computer-based infrared vision. We modify the
To date, some methods have been developed to meet this purpose. However, to date, a satisfactory solution has not been achieved for managing large-scale solar PV power plants. To
This paper based on U-Net network and HSV space, proposes a method of PV infrared image segmentation and location detection of hot spots, which is used
In this study, a lightweight real-time detection model, TA-YOLOv11, is proposed for UAV-based IR PV panel defect identification.
This study explores the potential of using infrared solar module images for the detection of photovoltaic panel defects through deep learning,
In this paper, the equipment used for collecting the infrared thermal images of PV panels was an infrared camera (FLUKE Ti 450), which is often used to acquire the thermal images of PV arrays in operation,