Abstract: Checking whether there are foreign objects in the new switch cabinet to ensure the safe operation of equipment is the basic task of distribution network construction. Aiming at the problem of complex background and serious target occlusion in the switch cabinet, a multi-scale residual convolution detection algorithm that integrates spatial information is proposed. Firstly, multi-scale residual convolution is used to reduce the impact of incomplete feature extraction caused by target occlusion, and then set the residual connection to solve the overfitting problem. Finally, the improved attention mechanism for channel and space integration is added between the deep feature maps, which reduces the effect of the loss of small target features caused by the network too deep, and improves the detection effect of small target objects. Eventually, a dataset of the switch cabinet for foreign-object detection experiment platform is made. In the experiment on the foreign body dataset of the self-made switchgear, the detection speed of the improved model decreased by 11FPS (Frames Per Second) to 72FPS, and the average accuracy AP₅₀ was 91.26%, which was 76.04% compared with the AP_{@50: 5:95}, which was increased by 2.59% and 3.69%, respectively. Experiments have proved that the detection method has a high detection accuracy and running speed, and has practical application value.