There is a high degree of color similarity between the individual camouflage target and the background, the target has a highly complex posture, and there are occlusion problems, which make individual camouflage target detection more challenging than traditional target detection. In order to solve the above problems, a depth learning algorithm based on polarization information and RGB (Red,Green,Blue) information is proposed, and the polarization image dataset CIP3K (Multicam type camouflage dataset and Woodland type camouflage dataset) is constructed. Based on Faster R-CNN (Faster Region-Convolutional Neural Network), a dual-stream feature fusion network TSF-Net is proposed, which can integrate target polarization feature information and RGB feature information. A large number of experiments are carried out on the CIP3K dataset to test the performance of the TSF-Net model and other detection models. The experimental results show that, compared with Faster R-CNN, the average detection accuracy of the TSF-Net model on the two datasets is increased by 8.2 percentages and 8.8 percentages, respectively, and is better than some mainstream object detection models.