运用基于改进的YOLOv5的交通形目标检测算法
Mingwu Wang1, Dan Qu2, Zedong Wu1
1Department of Mechanical Engineering, Shaanxi University of Technology, Hanzhong 723001, China.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
一个新的轻量级神经网络 (YOLOv5-Lite-s) 通过实现自动交通识别和定位来增强高速公路维护自动化. 该系统实现了高精度和速度,以实现高效的圆部署和收回操作.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 高速公路维护操作需要高效和自动化的交通部署和收回.
- 现有的系统可能缺乏实时操作所需的速度和准确性.
- 嵌入式系统为现场处理提供了潜力,但需要优化模型.
研究的目的:
- 开发和部署一个轻量级的神经网络,用于自动化交通识别和定位.
- 使用嵌入式设备提高高速公路维护操作的自动化水平.
- 为了满足交通放置和收缩的速度和准确性要求.
主要方法:
- 使用轻量级YOLOv5-Lite的神经网络与ShuffleNet骨干进行特征提取.
- 通过将卷积层替换为焦点模块并最大限度地减少C3层的使用,降低了计算复杂性.
- 在嵌入式设备上部署了优化的网络,以便实时识别和定位交通.
主要成果:
- YOLOv5-Lite网络在各种条件下 (距离,照明,遮蔽) 实现了约89%的识别准确度和每秒9 (fps).
- 该系统满足了在20公里/小时的车速下每分钟部署/检索30个的技术要求.
- 证明了自动交通放置和收回系统的准确和稳定的运行.
结论:
- 轻量级的YOLOv5-Lite-s网络有效地使机器视觉应用在交通回收操作中成为可能.
- 开发的系统增强了高速公路维护自动化,具有可接受的模型推断准确性和速度.
- 优化的神经网络适合在嵌入式设备上部署,用于实时流量管理任务.
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