Deep learning-driven underwater polarimetric target detection based on the dispersion of polarization characteristics

Underwater target detection especially in turbidity environments is a crucial and challenging area of study due to its extensive applications. In this work, we propose an underwater target detection method that combines the dispersion of polarization characteristics with a neural network framework. The polarization dispersion distribution features are effective at reducing the influence of the scattering effect of particulate matter in a turbidity environment, and highlighting the polarization state variations, especially in the edge contour features between the target and background. According to the physical detection model of dispersion of polarization characteristics, the neural network is constructed to extract dispersion values of the angle of polarization (AOP) and degree of linear polarization (DOLP) from the underwater polarization image datasets with different turbidity levels, which we then used to train the network to map physical characteristic parameters of the target for the detection. When compared to target detection methods based on light intensity images and polarization characteristic images (AOP and DOLP), the experimental results indicate that the proposed network model with polarization dispersion images has a significant improvement in locating and identifying targets of different materials, particularly in high turbidity underwater environments. These results provide deeper understanding of polarization polarimetric target detection, and further improve the functionality of a polarimetric target detection optical system.