PDT-YOLO:用于多尺度和封闭目标的路边物体检测算法
Ruoying Liu1,2,3, Miaohua Huang1,2,3, Liangzi Wang1,2,3
1Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China.
拟议的PDT-YOLO算法通过改进多尺度和封闭物体检测来增强路边感知. 这种智能系统实现了高精度和率,用于现实世界的交通部署.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 智能路边感知系统面临的挑战是多尺度物体,隐蔽目标和模型部署.
- 现有的算法在复杂的交通场景中扎着微弱的传感能力和高的错过检测率.
研究的目的:
- 提出PDT-YOLO算法,这是YOLOv7-tiny的增强,以解决路边物体检测的局限性.
- 提高多尺度和封闭对象的检测准确度,同时实现高效的边缘部署.
主要方法:
- 引入了规模内的特征交互模块 (AIFI) 和重建的特征金字塔,用于多规模的目标检测.
- 将轻量级卷积模块 (GSConv) 集成到高效的层聚合网络 (ETG) 中,以实现增强的特征提取.
- 整合了多重注意力机制和Wise-IoU,具有动态非单调的聚焦机制,以改善特征表达和模型概括.
主要成果:
- 在DAIR-V2X-C数据集 (mAP50: +4.6%,mAP50:95: +12.8%) 和IVODC数据集 (mAP50: +15.7%,mAP50:95: +11.1%) 上,PDT-YOLO显示出显著的改善.
- 该算法保持了低参数数量 (600万),适合边缘部署.
- 在使用 ROS 的真实交通环境中实现了 90 FPS 的检测率,满足了部署需求.
结论:
- 在智能路边感知中,PDT-YOLO有效地克服了以前模型在检测多尺度和封闭物体方面的局限性.
- 该算法为路边物体检测和在复杂的交通场景中进行边缘部署提供了强大的和高效的解决方案.
- 高检测率和提高的准确性使PDT-YOLO适用于实时智能运输系统.
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