同步端到端车辆行人检测算法基于复杂场景中改进的YOLOv8
Shi Lei1,2, He Yi1,2, Jeffrey S Sarmiento1
1Computer Engineering Department, Batangas State University, Batangas City 4200, Philippines.
Sensors (Basel, Switzerland)
|September 28, 2024
概括
本研究引入了改进的YOLOv8算法,用于在复杂的城市交通场景中增强车辆和行人检测,提高精度和实时性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 智能运输系统 智能运输系统
背景情况:
- 传统的车辆和行人检测方法在复杂的城市交通中面临局限性,包括尺度变化,阻塞和高计算成本,阻碍了准确性和实时应用.
- 对于先进的密集行人检测的需求需要更高的准确性,更低的计算开销,更快的速度,以及更容易部署的应用程序,如自动驾驶和公共安全.
研究的目的:
- 开发基于YOLOv8的改进的车辆和行人检测算法,以提高复杂交通环境中的检测精度和效率.
- 为了应对在目标检测中尺度变化和严重封闭的挑战.
- 为了实现适合智能运输系统的实时检测能力.
主要方法:
- 开发了一种改进的YOLOv8算法,结合可变形卷积骨干网络和注意力机制来优化网络结构.
- 引入了一个端到端的目标搜索算法,以提高车辆和行人检测的稳定性和准确性.
- 改进的模型被评估其精度,平均平均精度 (mAP) 和每秒 (FPS) 实时检测.
主要成果:
- 与基线方法相比,拟议的算法实现了精度增加11.76%和mAP增长6.27%.
- 该模型的实时检测速度为41.46 FPS,在复杂的场景中保证了强大的性能.
- 应用于智能运输系统,改进的YOLOv8模型达到了95.23%的mAP,性能优于YOLOv5,YOLOv7和更快的R-CNN.
结论:
- 改进的YOLOv8算法显著提高了车辆和行人检测的效率和稳定性,特别是在拥挤的城市环境中.
- 开发的模型为智能运输系统的实时检测提供了卓越的解决方案,满足了更高准确性和速度的需求.
- 整合可变形卷曲和注意力机制,以及端到端的目标搜索,为先进的交通场景分析提供了强大的框架.
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