Vehicle Detection and Tracking Method for Highway Fog Scene: Fusion Improvement of AG ‐ YOLOv10n and DeepSORT

ABSTRACT Fog presents a substantial latent hazard to highway traffic safety, significantly impairing drivers' visibility, thereby elevating the risk of high‐speed collisions. While driving, occlusion of multiple targets against complex backgrounds diminishes the detection rate of vehicle detectors. Many existing vehicle detection methods depend on bounding box representations for vehicle identification, limiting their capacity to provide accurate localization, particularly in foggy highway conditions. To enable early warnings of preceding vehicles in fog, this article proposes AG‐YOLOv10n, a novel vehicle detection method for foggy environments. This approach improves the model's adaptability to fog‐induced target features by replacing standard convolutional layers with AKConv and incorporating the GCAM gated convolutional attention module to enhance the extraction of locally salient information, thereby improving vehicle recognition accuracy in fog. Simultaneously, the DeepSORT tracking algorithm is enhanced, with AG‐YOLOv10n replacing the traditional Faster R‐CNN detector, and combined with the Kalman filter and Hungarian matching mechanism to achieve stable tracking of vehicle targets. The proposed method enhances the accuracy, recall rate, and average precision of the baseline model by 1.4%, 0.6%, and 1.1%, respectively, on the foggy vehicle dataset. The results demonstrate that the proposed method effectively improves detection accuracy, real‐time performance, and system robustness while maintaining the model's lightweight nature, which holds significant practical application for highway fog driving safety.