A target detection method based on infrared and visible image fusion is proposed to overcome the shortcomings of the existing target detection algorithms based on visible light. In this method, depth separable convolution and the residual structure are combined to construct a parallel high-efficiency feature extraction network to extract the object information of infrared and visible images, respectively. Simultaneously, the adaptive feature fusion module is introduced to fuse the features of the corresponding scales of the two branches through autonomous learning such that the two types of image information are complementary. Finally, the deep and shallow features are fused layer by layer using the feature pyramid structure to improve the detection accuracy of different scale targets. Experimental results show that the proposed network can completely integrate the effective information in infrared and optical images and realize target recognition and location on the premise of ensuring accuracy and efficiency. Moreover, in the actual substation equipment detection scene, the network shows good robustness and generalization ability and can efficiently complete the detection task.