Open Access

ARTICLE

Attention-Augmented YOLOv8 with Ghost Convolution for Real-Time Vehicle Detection in Intelligent Transportation Systems

Syed Sajid Ullah^1,*, Muhammad Zunair Zamir², Ahsan Ishfaq², Salman Khan¹

1 School of Energy and Electrical Engineering, Chang’an University, Xi’an, 710064, China
2 School of Information Engineering, Chang’an University, Xi’an, 710064, China

* Corresponding Author: Syed Sajid Ullah. Email:

Journal on Artificial Intelligence 2025, 7, 255-274. https://doi.org/10.32604/jai.2025.069008

Received 11 June 2025; Accepted 24 July 2025; Issue published 29 August 2025

Abstract

Accurate vehicle detection is essential for autonomous driving, traffic monitoring, and intelligent transportation systems. This paper presents an enhanced YOLOv8n model that incorporates the Ghost Module, Convolutional Block Attention Module (CBAM), and Deformable Convolutional Networks v2 (DCNv2). The Ghost Module streamlines feature generation to reduce redundancy, CBAM applies channel and spatial attention to improve feature focus, and DCNv2 enables adaptability to geometric variations in vehicle shapes. These components work together to improve both accuracy and computational efficiency. Evaluated on the KITTI dataset, the proposed model achieves 95.4% mAP@0.5—an 8.97% gain over standard YOLOv8n—along with 96.2% precision, 93.7% recall, and a 94.93% F1-score. Comparative analysis with seven state-of-the-art detectors demonstrates consistent superiority in key performance metrics. An ablation study is also conducted to quantify the individual and combined contributions of Ghost Module, CBAM, and DCNv2, highlighting their effectiveness in improving detection performance. By addressing feature redundancy, attention refinement, and spatial adaptability, the proposed model offers a robust and scalable solution for vehicle detection across diverse traffic scenarios.

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Keywords

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