Polarized light reflection information can directly invert the intrinsic characteristics of a target and has strong anti-interference characteristics in the transmission process. Thus, polarization imaging technology can be applied to the fields of intelligent monitoring and traffic monitoring in various complex environments. In recent years, deep-neural-network methods for interpreting image detection targets have been developed rapidly and widely used in various fields of image processing. In this study, a vehicle multi-target detection algorithm based on polarized images and deep learning is proposed. First, the target polarization degree image can be obtained by acquiring the polarization image in real time and analyzing the polarization information. Second, to enhance the high contrast between the detection targets and the background in the polarization image, channel attention and spatial attention are introduced into the backbone network to improve the ability of the network features to perform adaptive learning. In addition, the K-means algorithm is used to perform clustering analysis on the target location information, thereby increasing the network's learning speed in the polarization image and improving the progress of target detection. The experimental results show that this method is effective and fast for vehicle detection in complex scenes with low illumination. This method combines the advantages of polarization imaging and deep-learning target detection and has substantial application scope in road vehicle target detection, recognition, and tracking.