Accurate detection of vehicle and pedestrian targets is crucial for autonomous driving in foggy weather.Polarized images at 0°,45°,90°,and 135° were first acquired by a polarization imaging device,then I04590,stokes,and pauli image datasets were constructed through three different fusion methods.An improved YOLOv8 object detection algorithm was proposed to improve the detection accuracy of two types of targets,automobiles and pedestrians,in polarized images in foggy weathers.A MixSPPF structure based on hybrid pooling was proposed to improve the original SPPF structure's ability to extract global information.Then a Multi-scale Module was designed based on convolutions of different sizes and combined with the Coordinate Attention mechanism to enhance the extraction of spatial and channel information.The experimental results showed that the proposed improved YOLOv8 algorithm achieved the mean average precision(mAP)is mAP@0.5 value of 83.4%and mAP@0.5:0.95 value of 39.3%,which were improved by 1.6%and 0.9%respectively compared to the original YOLOv8 algorithm.
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