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In order to solve the problem of low accuracy of common target recognition results in polarized dark light scenes, a fusion algorithm based on convolutional neural network of polarization degree image and visible light image is proposed, a new loss function is designed to form an unsupervised learning process, the Laplace operator is introduced to improve the quality of fused image, and finally the polarization information of the target to be measured is effectively combined with the visible light information; and a fused target detection algorithm is proposed based on the improved YOLOv5 algorithm for target detection of the fused target, adding CA attention mechanism to the network framework, combining the channel attention mechanism with the spatial attention mechanism. The proposed network is trained and tested on a homemade dataset, and the results show that the fused image achieves better visual effects subjectively and objectively, comparing the improved YOLOv5 algorithm to the optimal YOLOv5s model, the precision and recall reach 89.3% and 82.5%, respectively, and the mean average precision is improved by 2.6% and 1.8%, respectively.