Implementation Framework for Deep Learning Network in Hot Spot Identification of Photovoltaic Panels

The proposed framework for photovoltaic panel hot spot identification demonstrates excellent stability and accuracy in various scenarios. Experimental results show that the framework accurately identifies hot spots in clear, unobstructed, cloudy, and low-light conditions. The accuracy for clear, unobstructed conditions reaches 99.2%, with an F1 score of 99.0%. Even in complex scenarios (such as early morning low light), the accuracy remains at 94.6%. The recognition speed is 22-28ms per image, meeting real-time monitoring requirements. Compared with traditional methods (threshold segmentation and SVM), the accuracy improves by 15.8%-20.1%. Compared with deep learning models (YOLOv5 and Faster R-CNN), the accuracy is 2.2%-2.7% higher, the recognition speed is 5.1-16.9ms per image faster, and the model size and training time are superior, demonstrating the efficiency and practicality of the framework for hot spot identification.