Polarization of road target detection under complex weather conditions

Polarization imaging technology can be applied to unveil the interaction between light and matter by harnessing the transverse vector wave attributes of light, thus to accentuate target characteristics amidst complex weather conditions. This technology has the potential to be widely used in road target detection. However, polarization detection is significantly affected by illumination and detection angles, as well as the considerable variation in the scale of road targets. The optimal polarization parameters should be adaptively adjusted to weather conditions, angles and target features, whereas most existing research employs handcrafted polarization parameters without considering actual complex detection requirements, which are unable to adaptively adjust the polarization feature enhancement methods. In this paper, we propose a road target detection algorithm based on an end-to-end adaptive polarization coding method, named YOLO-Polarization of Road Target Detection (YOLO-PRTD). To enhance the polarized features of targets under complex weather conditions, an Adaptive Polarization Coding Module (APCM) is designed. This module integrates channel-wise global self-attention and small kernel convolution to adaptively adjust the polarization enhancement method using dynamically extracted global and local polarization feature information. A multi-scale detection network is also designed to fully extract and fuse multi-scale feature information from receptive fields, channels, and spaces in different dimensions. Additionally, a dataset of Polarized Images of Road Targets in Complex Weather conditions (PIRT-CW) is proposed for training and evaluation. Experimental results on the PIRT-CW show that the YOLO-PRTD algorithm achieves a mAP0.5 of 89.83%, reducing the error rate by 15.54% compared to the baseline network YOLOX.