Polarization detection technology can highlight targets in complex background environments, providing us with clearer and more accurate target recognition. However, research on the use of polarization imaging in courtroom science, regarding the detection and search for underwater evidence, is lacking. To address this issue, this study fuses the polarization and target intensity images using a polarization imaging device. After decomposing the images using non-subsampled shear waves (NSST) into low and high-frequency sub-bands, a simplified impulse-coupled neural network model with adaptive parameters is proposed for the high-frequency sub-band, and an adaptive weighting fusion rule, based on region energy, is used for the low-frequency sub-band. Correlation algorithm comparison experiments were conducted for three typical targets at visible wavelengths. The experimental results show that underwater evidence can be effectively detected using polarization imaging technology. The image fusion algorithm proposed in this paper effectively highlights the detailed features of underwater evidence, verifying the effectiveness of polarization detection technology for underwater evidence imaging, which is conducive to breaking through the existing research gap in the field of courtroom science.