Accurately locating the instrument position of power distribution cabinet through machine vision is the key to realize intelligent identification of instruments. Aiming at the problem of low target positioning accuracy caused by complex background of power distribution cabinet， various character scales and small camera pixels， a YOLOv7-MSBP target location algorithm for character recognition of power distribution cabinet was proposed. Firstly， a Micro-branch detection branch was designed and the initial anchor box laying interval was changed to improve the detection accuracy for small targets. Secondly， Bi-directional Feature Pyramid Network （BiFPN） was introduced to fuse the feature values of different layers across scales， thereby improving the situations of the loss of detailed features and insufficient feature fusion caused by downsampling. Meanwhile， Synchronous Convolutional Block Attention Module （Syn-CBAM） was designed， channel and spatial attention features were fused with weights， then the feature extraction ability of the algorithm was improved. And a Partial Convolution （PConv） module was introduced in the backbone network to reduce model redundancy and delay， and increase detection speed. Finally， the positioning results of YOLOv7-MSBP were sent to Paddle OCR （Optical Character Recognition） model for character recognition. Experimental results show that the mean Average Precision （mAP） of YOLOv7-MSBP algorithm reaches 93.2%， which is 4.3 percentage points higher than that of YOLOv7 algorithm. It can be seen that the proposed algorithm can locate and recognize the characters of the power distribution cabinet quickly and accurately， which verifies the effectiveness of the proposed algorithm.