Image Enhancement for Better VRU Detection in Challenging Weather Conditions

This paper introduces a new approach to improve the performance of object detection techniques in challenging weather conditions through image enhancement. In 2018, a tragic accident occurred while testing a self-driving vehicle under severe bad lighting conditions, where a person crossing the street with a bicycle was not detected early enough for the vehicle to take action, which led to the death of the cyclist. This shows the dire need for robust, fast, and efficient detection techniques in adversarial conditions. In this paper, we propose a novel processing pipeline that contains a multilabel classifier trained to capture quality issues in a given frame and then passes it to proper filters to enhance the quality. The image qualities we target in this study include low visibility/bad lighting conditions, blurring, and image distortion. The raw frame goes through a multi-label classifier first to detect possible quality degradation. The frame then passes through various filters to fix the detected qualities accordingly. Performance evaluation shows that the proposed multilabel classifier achieves 80% accuracy during testing with a 20-ms average inference time for each frame. The proposed pipeline also enhances the frame processing delay by 3x and increases the confidence score of the YOLO detection algorithm by 5% on average.