使用YOLO进行高效的交通信号识别,用于智能交通系统
Cong Wang1, Bin Zheng2, Chenxing Li3
1School of Information and Electrical Engineering, Panzhihua University, Panzhihua, 617000, China.
Scientific reports
|April 20, 2025
概括
本研究介绍了一种优化的YOLOv5交通标志识别 (TSR) 框架,改进了小型目标检测和自动驾驶的实时性能. 改进后的系统在各种条件下实现了高精度和稳定性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 自主系统 自主系统
背景情况:
- 交通标志识别 (TSR) 对自动驾驶安全至关重要.
- 现有的系统面临着小目标,环境变化和实时处理的挑战.
研究的目的:
- 开发一个基于YOLOv5的优化框架,以实现强大高效的TSR.
- 为了增强检测小型交通标志,并提高在各种环境条件下的性能.
主要方法:
- 实现了k-means++用于箱优化,以改善小目标检测.
- 对YOLOv5变体 (s/m/x) 进行了比较分析,用于精确速度的权衡.
- 进行了系统的超参数调整,以提高强度.
主要成果:
- 在CCTSDB数据集上实现了98.1%的平均平均精度 (mAP) 和99.3%的精度.
- 在mAP中以5-8%的速度超过Faster-RCNN和SSD,同时保持45FPS.
- YOLOv5s变体表现出最佳平衡,99.3%的mAP@0.5和32毫秒的推断时间.
- 在雾和背光等具有挑战性的场景中展示了强大的性能.
结论:
- 优化的YOLOv5框架显著提高了TSR的准确性和可靠性.
- 该系统非常适合用于智能运输系统中的实时应用.
- 提出的优化方案为自动驾驶提供了灵活和高性能的解决方案.
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