A Feature Fusion Method to Improve the Driving Obstacle Detection Under Foggy Weather

Abstract

Driving obstacle detection has great importance in automatic driving safety. However, the deterioration of image quality in the foggy environment brings many difficulties to the detection and even endangers driving safety. Therefore, in this article, a feature fusion method is designed to improve the performance of the camera sensor in driving obstacle detection under foggy weather. The difference in image features of the same traffic scene on sunny and foggy days is compared, and the reason for the decrease in detection performance of the camera sensor in foggy environments is found. The foggy image dataset is constructed, and the feature fusion of the detection object in foggy and sunny environments is realized through YOLOv3. Compared with the regular trained model, the method in this article improves 30% in mean average precision (mAP), 40% in recall, and 22% in F1-Score of the object detection performance in foggy images. Unlike the image defogging method, foggy images can be directly used for detection and have higher detection accuracy and time-efficient. This is of great significance for improving driving obstacle detection in adverse weather, which is important to ensure driving safety.