When an inspection robot is applied in the outdoor substation, there exists a problem of low accuracy of pointer meter recognition in complex environment. This paper proposes a pointer meter recognition method based on gray-level dynamic adjustment and Blackhat-Otsu algorithm. Aiming at the foggy environment, the Retinex dehazing algorithm based on gray level dynamic adjustment is proposed to process foggy images with different concentrations and the image contrast and clarity are improved. The information entropy of the obtained image is increased by 1.1 dB--2 dB compared with that of other dehazing methods, but the mean square error (MSE) is reduced by 700--800. The fast guided filter layer is introduced in the ResNet network deraining model to remove the rain pattern on the image, and the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) are both improved. In order to improve the accuracy of pointer reading, the Blackhat-Otsu pointer separation method is proposed to eliminate the interference of pointer shadow and dial scale. The experimental results show that the proposed method has good robustness to the rain-fog environment in the substation, and improves the accuracies of instrumental detection and reading recognition.