Abstract
YOLO (You Only Look Once) is a high-precision real-time object detection algorithm widely adopted in the visual perception of self-driving cars. However, its performance will be dramatically blocked in raining and foggy conditions. In order to address the detection of vehicles and pedestrian targets of self-driving vehicles during their driving on the road, this paper puts forward an improved YOLO model based on Multiscale Retinex with Colour Restoration defogging algorithm. In the proposed approach, different YOLO models (including YOLOv2, YOLOv3, YOLOv4 models) are fabricated to obtain a eligible network for vehicle and pedestrian detection in foggy environments. The experimental results indicate that the aforementioned three YOLO models have better performance based on the hypothesized approach, validating that the approach is effective in raining and foggy conditions. In addition, the YOLOv4 network model trained by the MSRCR algorithm enhances the accuracy of detection. Therefore, it can be estimated that the proposed approach is eligible for the application of real-time object detection in self-driving cars.
Published Version
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