Abstract: In order to improve the performance of visible light object detection in complex scenes, a visible light object detection algorithm adaptively fused with infrared features is proposed by combining deep convolutional neural network with multi-source information fusion technology. The algorithm takes infrared and visible images as input, extracts infrared and visible features by means of convolution and activation combined with residual structure, and uses spatial and channel attention mechanisms to improve the category of the target and the feature weight of the image region where the target resides. Secondly, the extracted infrared features are incorporated into the visible features of the corresponding dimension in the way of adaptive weighting, which fully makes up for the limitation of the object in the single-mode model; Finally, for multi-scale objects, a pyramid sampling structure is designed. By alternating up-sampling and down-sampling, the global and local features of the feature are fully integrated to enhance the scale invariance of the network. Experiments show that the proposed attention mechanism, feature adaptive fusion and pyramid sampling structure can effectively improve the effect of object detection. Compared with the same type of infrared visible light object detection method, this method can fully integrate the multi-modal features of the object, effectively reduce noise interference, and make the network have higher detection performance. At the same time, in the actual power grid equipment detection, this method also shows high generalization ability and robustness, and can accurately and efficiently achieve the identification and positioning of object equipment.