TY  - EJOU
AU  - Ullah, Syed Sajid 
AU  - Zamir, Muhammad Zunair 
AU  - Ishfaq, Ahsan 
AU  - Khan, Salman 

TI  - Attention-Augmented YOLOv8 with Ghost Convolution for Real-Time Vehicle Detection in Intelligent Transportation Systems
T2  - Journal on Artificial Intelligence

PY  - 2025
VL  - 7
IS  - 1
SN  - 2579-003X

AB  - 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.
KW  - YOLOv8n; vehicle detection; deformable convolutional networks (DCNv2); ghost module; convolutional block attention module (CBAM); attention mechanisms

DO  - 10.32604/jai.2025.069008