In the outdoor inspection task of substation, the robot is vulnerable to the complex environment of outdoor wind, fog and uneven road surface. It is prone to jitter and angle deviation, resulting in blurred photos, instrument tilt and other problems, and it is difficult to ensure the accuracy of the identification reading of the pointer instrument. In order to solve this problem, combined with YOLOX target detection, DeblurGAN-v2 image enhancement, DeepLabV3+ semantic segmentation neural network algorithm, a fuzzy pointer meter reading correction and recognition method is proposed. Firstly, the YOLOX network is improved to extract the instrument panel, pointer area and instrument text information, and obtain the instrument parameters. Secondly, enhance the feature extraction ability of DeblurGAN-v2 network to remove the fuzzy influence in the image, then use deeplabv3+ network to divide the dial and pointer. In the aspect of image correction, perspective change and text rectangle correction are used to achieve high-precision correction of the instrument.Experiments show that this method can more effectively solve the impact of complex environment in the detection task, and the detection accuracy is as high as 97.55%, which meets the requirements of automatic detection in industry.