增强的实时高速公路对象检测用于施工区安全使用YOLOv8s-MTAM

Wen-Piao Lin^1,2, Chun-Chieh Wang¹, En-Cheng Li¹

¹Department of Electrical Engineering, Chang Gung University, Taoyuan 33303, Taiwan.

Sensors (Basel, Switzerland)

|October 29, 2025

概括

这项研究通过使用改进的YOLOv8s系统与运动时间注意模块 (MTAM) 来增强自动驾驶对象检测. 该系统在动态高速公路建设区识别危险时达到高精度.

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:

对象检测对于自动驾驶安全至关重要,特别是在动态高速公路建设区.
现有的系统与这些环境中常见的高速,封闭或模糊对象作斗争.

研究的目的:

开发一个增强的YOLOv8s物体检测系统,以提高在施工区自动驾驶的稳定性.
整合一个运动时态注意模块 (MTAM) 来更好地检测动态和封闭的物体.

主要方法:

一个增强的YOLOv8s架构,包括一个CSP骨干,FPN-PAN功能融合,以及先进的损失功能.
整合了一种新型的运动时间注意力模块 (MTAM),利用时间卷曲和注意力机制.
在一个由34,240张图像组成的定制数据集上进行培训,这些图像具有广泛的数据增强和9-Mosaic转换.

主要成果:

实现了高性能指标:mAP ((IoU[0.5]) 的90.77 ± 0.68%和mAP ((IoU[0.5:0.95]) 的70.20 ± 0.33%.
证明了强大的现实世界识别率:建筑车辆96%;警告标志92%;旗84%.
MTAM有效地提高了对模糊和部分遮蔽物体的检测.

结论:

增强的YOLOv8s系统与MTAM显著提高了对象检测可靠性,在具有挑战性的高速公路建设区.
该框架显示了智能交通系统实时部署的巨大潜力,以提高安全性.

关键词:

这就是YOLOv8的意义.自动驾驶自动驾驶的自动驾驶.建筑车辆建筑车辆数据增强数据增强运动时间的注意力.对象检测检测对象检测对象检测警告标志警告标志警告标志

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