S2DB-mmWave YOLOv8n:用于毫米波雷达的多物体检测,使用优化多尺度功能的YOLOv8n
Mengqi Yuan1,2, Yajing Yuan1,3, Xiangqun Zhang1,3
1School of Information Engineering, Xuchang University, Xuchang, China.
PloS one
|September 19, 2025
概括
本研究介绍了S2DB-mmWave YOLOv8n,这是一种用于毫米波 (mmWave) 雷达物体检测的深度学习框架. 与基线YOLOv8n.n.相比,增强的模型显著提高了多目标检测准确度和分类.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 雷达技术 雷达技术的使用
背景情况:
- 毫米波 (mmWave) 雷达提供全天候,保护隐私的物体检测,对于智能安全和运输至关重要.
- 现有的毫米波雷达物体检测在区分多个目标和算法性能方面面临挑战.
- 深度学习方法对于推进毫米波雷达能力至关重要.
研究的目的:
- 为毫米波雷达提出基于深度学习的准确目标检测和分类框架.
- 增强特征提取,细节恢复和特征融合在毫米波雷达物体检测中的功能.
- 解决多目标歧视和检测性能方面的局限性.
主要方法:
- 开发了一个新的骨干网络,具有新的卷积层和简化空间金字塔聚合 - 快速 (SimSPPF) 模块.
- 集成了一个动态上采样技术,以改善细节回收.
- 整合了双向特征金字塔网络 (BiFPN) 以优化特征融合.
主要成果:
- S2DB-mmWave YOLOv8n模型实现了93.1%的mAP@0.5,55.8%的mAP@0.5:0.95,89.4%的精度和90.6%的回忆.
- 与基线YOLOv8n网络相比显著改善 (3.3%,1.6%,4.5%和7.7%分别更高).
- 在不增加模型的参数数量的情况下,可以实现性能增长.
结论:
- 拟议的S2DB-mmWave YOLOv8n框架为毫米波雷达对象的检测和分类提供了卓越的性能.
- 新的架构增强有效地应对多目标场景中的挑战.
- 这一框架对于智能安全和运输应用具有重大实际价值.
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