With the rapid development of power system intelligence and unattended substations, intelligent inspection robot play an increasingly important role in equipment operation state perception and safety monitoring. Aiming at the problems of missed detection and false detection in the meter detection process of the inspection robot in the substation environment with complex lighting conditions and changing target scales, this study proposes a pointer meter detection model of substation intelligent inspection robot (SRPMNet). By constructing the average pooling down sampling (ADown) module, the computational complexity is reduced and the detection accuracy of the small target meter is improved. A lightweight attention mechanism is designed to highlight key feature information and suppress irrelevant background interference. In order to further improve the accuracy and feature expression ability of pointer meter detection in complex environment of substation, this study constructs an auxiliary detection head strategy. At the same time, the wise complete intersection over union (Wise-CIoU） is proposed in the boundary frame regression process to improve the positioning accuracy and convergence stability of the model in the complex background of the substation. This study builds a substation intelligent inspection robot platform for data collection. The experimental results show that the mAP@0.5 index of the model on the self-built substation pointer meter dataset and the public industrial meter dataset reaches 91.7% and 90.4%, respectively, which is 3.0% and 5.3% higher than the baseline model. It effectively solves the problem of missed detection and false detection, and provides effective technical support for substation intelligent inspection tasks.