融合注意力与轻量化模块，提升交通场景下车辆检测精度与速度
Attention-Augmented YOLOv8 with Ghost Convolution for Real-Time Vehicle Detection in Intelligent Transportation Systems
- 用轻量模块减少冗余特征，结合注意力机制增强关键区域识别
- 在KITTI数据集上达到95.4% [email protected]，较基线提升8.97%
- 适合部署于实时交通监控系统，兼顾性能与计算效率
精准车辆检测是自动驾驶、交通监控和智能交通系统的关键。本文提出一种基于YOLOv8n的增强模型，融合了Ghost模块、卷积块注意力模块（CBAM）和可变形卷积网络v2（DCNv2），以提升检测性能。其中，Ghost模块通过高效特征生成减少冗余；CBAM通过通道与空间注意力精炼特征表示；DCNv2增强对车辆结构几何变化的适应能力。在KITTI数据集上的评估显示，该模型[email protected]达95.4%，较基线提升8.97%，同时实现96.2%精度、93.7%召回率和94.93% F1分数。与七种先进检测器对比，本方法在关键指标上持续领先，消融实验验证了各模块的独立及协同贡献。该方案通过解决特征冗余、注意力优化与空间适应性问题，为复杂交通环境下的车辆检测提供了高效可靠的解决方案。
原文摘要 · Abstract (English)
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% [email protected], 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.
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