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提高自动驾驶的YOLOv5:在边缘设备上有效的基于注意力的对象检测
Mortda A A Adam1, Jules R Tapamo1
1School of Engineering, Howard College Campus, University of KwaZulu-Natal, Durban 4041, South Africa.
Journal of imaging
|August 27, 2025
概括
这项研究引入了用于自动驾驶的轻量级物体检测模型,通过注意力机制增强了YOLOv5. BaseECAx2模型提供了高效的边缘部署,而BaseSE-ECA在关键车辆检测任务中实现了高精度.
科学领域:
- 计算机视觉
- 人工智能
- 自主系统
背景情况:
- 对象检测对于自动驾驶的安全性和效率至关重要.
- 对于边缘设备而言,深度学习模型是有效的,但也很昂贵.
- 需要轻量化,高性能的物体检测模型.
研究的目的:
- 开发轻量级的物体检测模型,用于边缘设备的实时自动驾驶.
- 将高级道注意力策略 (ECA,SE) 集成到YOLOv5s架构中.
- 在KITTI和BDD-100K等标准数据集上评估模型性能.
主要方法:
- 使用YOLOv5s架构作为轻量级物体检测的基础.
- 集成的高效通道注意力 (ECA) 和挤压和刺激 (SE) 注意力模块.
- 在KITTI和BDD-100K数据集上训练和评估了四种不同的模型.
- 使用精度,回忆和平均精度 (mAP) 等指标评估性能.
主要成果:
- BaseECAx2模型实现了最低的GFLOP (13) 和最小的尺寸 (9.1 MB),非常适合边缘设备.
- 根据BaseSE-ECA模型,车辆检测的准确度高达96.69%和98.4% mAP.
更多相关视频
结论:
- 具有注意力机制的轻量级YOLOv5s型号为自动驾驶提供了性能和效率的平衡.
- 基于BaseECAx2和BaseSE-ECA模型提供了实时边缘部署的成本效益解决方案.
- 需要进一步的研究来改善复杂的现实驾驶场景的稳定性.


