Vehicle and driver detection in real-world traffic scenarios with an improved YOLO model

Abstract

In recent years, vehicle and driver detection in real-world traffic scenarios has attracted increasing attention, with accurate driver identification being critical for traffic supervision and public safety. This paper proposes IYOLO, an enhanced YOLOv8-based framework for simultaneous detection and classification of vehicles, drivers, and passengers on highways, aiming to distinguish drivers from passengers and establish one-to-one vehicle-driver associations. The model leverages Cross Stage Partial to Fast (C2f) modules to reduce redundant computations and accelerate inference, and employs an optimized feature pyramid with multi-scale fusion to improve small-object detection. An adaptive Label Smoothing Regularization strategy enhances generalization and classification robustness, while Online Hard Sample Mining focuses learning on challenging samples during training, improving feature discrimination and overall performance under complex conditions. Extensive experiments on the PSD-HIGHROAD dataset demonstrate that IYOLO consistently outperforms state-of-the-art methods in detecting and classifying vehicles, drivers, and passengers, achieving superior accuracy and robustness across varying lighting, poses, and traffic conditions.