Road Object Detection in Foggy Weather Based on Polarization Imaging and YOLOv8
TAN Ailing1, LI Xiaohang1,ZHAO Yong2,GAO Meijing3,SU Haijie1,LIU Chuang1,GUO Tianan1
1. The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province, School of Information and Science Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
2. The Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
3.College of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100081,China
Abstract：Detection of automobile and pedestrian targets in foggy weather is of interest to the field of autonomous driving. 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 structures 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@0.50.95 value of 39.3%, which were improved by 1.6% and 0.9% respectively compared to the original YOLOv8 algorithm.
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2024, Vol. 45 
