An approach based on deep learning methods to detect the condition of solar panels in solar power plants

Solar panels are increasingly popular due to global energy shortages and rising costs. However, managing large or elevated panel systems requires regular oversight, leading to potential time and cost challenges. This study was focused on developing an AI-based drone for panel detection to address these issues and facilitate the control process. A low-cost system for AI-based identification of dusty, broken, and healthy solar panels was created using a Raspberry Pi 4B board and camera. The study proposed a Histogram Equalization (HE)-based preprocessing technique to improve the AI model. Firstly, the trainings were performed with YOLOv5 without the proposed method at epoch values of 100, 150, and 200 in order to see the effectiveness of the proposed method more clearly. As a result of these trainings, the highest F1 score was obtained as 80 %. In the second step, three deep learning algorithms - YOLOv5, YOLOv7, and YOLOv8 - with epochs of 100, 150, and 200 respectively, were used for training with the proposed method. A detailed comparative analysis of the developed models was carried out regarding their performance metrics. The YOLOv5l was obtained as the most successful panel detection model with an F1 score of 97 % at 150 epochs. The model with the best performance metrics was used in a real-time test application with an AI-based drone. F1 score results were obtained between 90 % and 97 %, mainly supporting the success rate obtained in real-time application. The results strongly support the effectiveness of this proposed method for panel detection tasks, showcasing its high efficacy and promising potential.