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

TL;DR

This paper enhances YOLOv8 for real-time vehicle detection by integrating Ghost Module, CBAM, and DCNv2, achieving significant accuracy improvements on the KITTI dataset.

Contribution

It introduces a novel combination of modules into YOLOv8 to improve detection accuracy and efficiency in traffic environments.

Findings

Achieved 95.4% mAP@0.5 on KITTI dataset, 8.97% higher than baseline.

Demonstrated superior performance over seven state-of-the-art detectors.

Ablation studies confirmed the effectiveness of each integrated module.

Abstract

Accurate vehicle detection is a critical component of autonomous driving, traffic surveillance, and intelligent transportation systems. This paper presents an enhanced YOLOv8n-based model that integrates the Ghost Module, Convolutional Block Attention Module (CBAM), and Deformable Convolutional Networks v2 (DCNv2) to improve detection performance. The Ghost Module reduces feature redundancy through efficient feature generation, CBAM refines feature representation via channel and spatial attention, and DCNv2 enhances adaptability to geometric variations in vehicle structures. Evaluated on the KITTI dataset, the proposed model achieves 95.4% mAP@0.5, representing an 8.97% improvement over the baseline YOLOv8n, along with 96.2% precision, 93.7% recall, and a 94.93% F1-score. Comparative analysis against seven state-of-the-art detectors demonstrates consistent superiority across key performance metrics, while ablation studies validate the individual and combined contributions of the integrated modules. By addressing feature redundancy, attention refinement, and spatial adaptability, the proposed approach offers a robust and computationally efficient solution for vehicle detection in diverse and complex traffic environments.