YOLOv8m enhancement using α-scaled gradient-normalized sigmoid activation for intelligent vehicle classification

Abstract

Vehicle classification plays a vital part in the development of intelligent transportation systems (ITS) and modern traffic management, where the ability to detect and identify vehicles accurately in real time is essential for maintaining road efficiency and safety. This paper presents an enhancement to the YOLOv8m model by refining its activation function to achieve higher accuracy and faster response in diverse traffic and environmental situations. In this study, two alternative activation functions—Mish and Swish—were integrated into the YOLOv8m structure and tested against the model’s default sigmoid linear unit (SiLU). Training and evaluation were carried out using a comprehensive dataset of vehicles captured under different lighting and weather conditions. The experimental findings show that the modified activation design leads to better model convergence, improved generalization, and a noticeable boost in detection performance, recording up to 5.4% higher accuracy and 6.6% better mAP scores than the standard YOLOv8m. Overall, the results confirm that fine-tuning activation behavior can make deep learning models more adaptive and reliable for vehicle classification tasks in real-world intelligent transportation environments.