An Infrared Image Detection of Substation Equipment integrating Selective Deblurring and Center2Net

The current Unmanned Aerial Vehicle (UAV) inspection scheme lacks infrared image preprocessing to deblur the image, and the detection robustness is not high in complex backgrounds. A method for detecting infrared images of substation equipment is proposed based on selective deblurring and anchor-free detectors in this study. The selective deblurring algorithm, based on DeblurGANv2, is used to determine and process deblurring for infrared images. Subsequently, an optimized anchor-free detection model is introduced, and the enhanced bottleneck module is used to optimize CenterNet, improving feature extraction capabilities and robustness. Lastly, excellent detection results are further obtained by improving the depth of the model backbone network. The results demonstrate that the proposed method effectively improves motion blur issues in UAV image capture, significantly improves detection accuracy, and enhances the efficiency and reliability of inspection for substation equipment.