Enhancing PV panel diagnosis: RT-DETR vs YOLO v8 for real-time hotspots detection

Accurate and efficient detection of hotspots in photovoltaic (PV) panels is paramount for guiding maintenance decisions, enhancing power generation, and ensuring the safety and stability of power stations. In this context, this study applies the Real-Time Detection Transformer (RT-DETR) for the real-time identification of hotspots on PV panels to improve their performance. The use of RT-DETR eliminates the need for traditional post-processing methods like Non-Maximum Suppression (NMS), leading to faster and more accurate detection. A comparative analysis reveals that RT-DETR stands out with zero postprocessing time, whereas YOLO v8 requires over 1.0 ms for similar image analysis tasks. Notably, RT-DETR achieves a higher mean Average Precision (mAP) of 0.802, compared to YOLO v8-l's 0.673, despite the latter having more parameters. This points to RT-DETR's superior efficiency in both speed and accuracy. Additionally, RT-DETR's F1 score of 88% further demonstrates its precision, surpassing that of YOLO v8-l, while maintaining a high frame rate. These results confirm RT-DETR as an effective tool for real-time hotspot detection, suitable for operation on low-power devices such as drones and infrared-equipped smartphones, offering a practical solution for areas with limited resources. The study showcases the potential of RT-DETR in enabling rapid and accurate hotspot detection in PV panels, a significant step forward in photovoltaic technology management.