Defect Detection Method for Hydropower Station Equipment Based on Infrared Temperature Measurement Data

To address the challenges of low accuracy, low efficiency, and significant time and labor costs of the current defect detection process for hydropower station equipment, a defect detection algorithm for hydropower station equipment based on the attention mechanism of infrared temperature measurement data is proposed. First, a new attention module, Coordinate Attention (CA), is introduced into the YOLOv5s feature extraction network to better capture key information in the input and improve the model performance and accuracy. Second, we replaced the ordinary convolution and C3 modules in YOLOv5 with Ghost Revolution and Bottleneck Transformer to reduce the model's parameters and computational complexity. Finally, the defect detection algorithm is combined with the PyQt5 interface to achieve visual image detection and output detection results. The experimental results show that the improved YOLOv5 model's mAP@0.5 is increased by 6.3%, and P and R are increased by 6.6% and 5.9%, respectively, meeting the detection needs of hydropower station equipment defects and achieving a higher detection accuracy.