Object Detection in Foggy Weather using Deep Learning Model

This study addresses the challenge of accurate object detection in foggy environments, a critical issue in computer vision. We propose a novel approach using a real dataset collected from diverse foggy weather conditions, focusing on varying fog densities. By annotating the dataset from Real-Time Traffic Surveillance (RTTS) and using the YOLOv8x architecture, we systematically analyze the impact of fog density on detection performance. Our experiments demonstrate that the YOLOv8x model achieves a mean average precision (mAP) of 78.6% across varying fog densities, outperforming state-of-the-art methods by 4.2% on the augmented dataset. Additionally, we show that increased dataset diversity significantly enhances the robustness of the model in detecting objects under challenging foggy conditions. Our research contributes to advancing object detection systems tailored for foggy environments, with implications for safety and efficiency in domains like autonomous driving and surveillance.