A Lightweight YOLO V5 Method for Detecting Thermal Spot Defects in PV Panels in Infrared Images

As an important component of photovoltaic power generation, PV panels play a crucial role in the photovoltaic power generation industry. In order to overcome the current problem of low speed and accuracy in detecting hot spot faults of PV panels in photovoltaic power plants, this paper proposes a lightweight YOLO V5 model to realize the detection of hot spot defects of PV panels in infrared images. First, data enhancement is performed on the existing dataset so as to increase the number of samples i n the database and enhance the generalization ability of the model. Second, a lightweight network structure Shufflenet v2 is introduced to replace the YOLO V5 backbone network to enhance the speed of detection. Finally, the attention mechanism is combined with the neck network to enhance the extraction of hot spot features. It can effectively balance the network's speed and accuracy. The experimental results show that the improved network achieves recognition accuracy of 93.5% for hot spots, which is 4.4% higher compared to the original network.