A new scheme of vehicle detection for severe weather based on multi-sensor fusion. (15th March 2022) - Wang, Zhangu; Zhan, Jun; Li, Ye; Zhong, Zhaohui; Cao, Zikun
Highlights: Data-driven radar target extraction method improves accuracy and convenience. Vehicle ROI extraction based on radar-guided information. Improved Haar-like feature for vehicle detection. Radar and infrared image fusion for vehicle detection in severe weather. Abstract: Automated vehicles are prone to traffic accidents in severe weather conditions. Real-time vehicle detection can improve the driving safety of automated vehicles. This paper proposes a new vehicle detection method based on multi-sensor fusion to improve the vehicle detection performance in severe weather conditions. First, an efficient vehicle target extraction method from the radar is proposed that uses supervised learning to train a classifier based on LightGBM. This method does not require complex prior knowledge to determine the target segmentation threshold and transforms the target extraction into a data-driven classification. The vehicle target extraction method based on LightGBM has 95.5% accuracy and a 96% true positive rate. Second, we estimate the potential area of vehicles from infrared images according to the distribution of radar targets and predict the region of interest (ROI) of vehicles based on pixel regression. The ROI extraction method based on radar can avoid complicated calculations and interference of heat sources in the environment, which will greatly improve the speed and accuracy of ROI extraction. Radar-based ROI extraction only takes 4 ms, which is much lower than image-based ROI extraction. Finally, four new Haar-like feature templates are designed to improve the vehicle detection performance, which can improve the detection accuracy by 2.9%. This method has a 92.4% detection accuracy and a 43 Fps detection speed in the road test, which significantly improves the vehicle detection performance in severe weather.