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Polarization imaging has the advantage of detecting artificial targets based on their intrinsic characteristics. However, with the development of camouflage materials and camouflage shielding performance, the anti-optical detection technology for camouflaged targets continues to improve. In this paper, we combine the advantages of polarization imaging and deep learning to achieve rapid detection of artificial targets camouflaged in natural scenes. Firstly, we propose a Stokes-vector-based parameter image to show the polarization specificity of the camouflaged artificial targets. Then, a detection method is proposed, which uses an Otsu segmentation algorithm and morphological operations to extract polarization signatures of the target from the proposed parameter image, and utilizes the extracted polarization signatures to highlight the camouflaged artificial targets. Finally, we improve a self-supervised deep learning network to enhance the low-light images, extending the application of our method into low illumination environment target detection. Experimental results demonstrate that our method can effectively detect the camouflaged artificial targets with a detection rate better than 80%, which has potential application value in the fields of military target detection, security monitoring, and remote sensing.

Rapid Detection of Camouflaged Artificial Target Based on Polarization Imaging and Deep Learning