In order to solve the problem of missed detection and false detection caused by occlusion in the process of target detection, a military target detection algorithm with image restoration module is proposed, which realizes the automatic recognition of military targets through deep learning, and then combines image restoration to achieve image enhancement of occluded targets.For the target detection module, a convolutional attention mechanism is added on the basis of YOLOv4 to enhance the sensitivity to target recognition and the feature extraction ability of the network.And the cross-iterative batch standard layer is adopted to improve the training efficiency of the model.The image restoration module is a double generator model designed based on the generation confrontation network.Considering that the integrity of the target image outline has a certain impact on image restoration and target detection, an edge generation network is added.The image restoration module aims to restore the part of the target that is occluded.The experimental results show that the target detection accuracy of the target detection algorithm with image restoration module reaches 79.63%, which effectively solves the problem of missed detection and false detection in the case of occlusion.