Style transfer and recognition of infrared images of transformers under different lighting based on CycleGAN

Abstract

Abstract In order to solve the problem that the transformer in the infrared image of the transformer is difficult to be accurately detected due to strong light irradiation under different lighting conditions, a method for unifying the illumination and equipment identification of the infrared image of the transformer based on CycleGAN and yolov3 is proposed. CycleGAN is used to unify the illumination intensity of the infrared image of the transformer, and an adversarial loss function and a cycle consistency loss function are introduced to ensure high-quality generation of infrared images, and the expanded infrared sample library is richer. Finally, the yolov3 network is trained using the uniformly illuminated image set to achieve accurate identification of transformers. The experimental results show that the infrared image of the transformer that has been unified by illumination can identify the transformer with an accuracy of 95.2%, which is 3.1% higher than before enhancement.