混合YOLO-ViT模型带有数字地图,用于在实时道路场景中高效地检测小型交通标志
R Suresha1, N Manohar1, M Priyanka1
1Department of Computer Science, School of Computing, Mysusru Campus, Amrita Vishwa Vidhyapeetham, India.
MethodsX
|November 24, 2025
概括
这项研究引入了一种混合YOLO-ViT模型,用于实时检测微小的交通标志,提高准确性并减少印度道路条件的计算负载.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 实时微小的交通标志识别至关重要,但面临着高分辨率图像要求和计算成本的挑战.
- 图像中的上下文特征丢失导致错误检测,特别是在小型交通标志上.
研究的目的:
- 设计一个实时交通标志检测 (TSD) 框架,以应对印度道路环境的挑战.
- 通过地图集成和新型混合架构来降低计算成本并最大限度地减少错误检测.
- 通过结合本地和全球上下文特征来提高微小TSD的准确性.
主要方法:
- 一个新的混合YOLO-ViT框架,将YOLOv11n骨干与视觉转换器 (ViT) 集成在一起.
- 利用数字地图和精确的车辆定位来定位搜索空间并定义感兴趣的区域.
- 将本地特征提取 (YOLOv11n) 与全球上下文特征捕获 (ViT) 结合起来.
主要成果:
- 在印度的交通标志数据集上实现了97.5%的精度,96.4%的回忆,96.1%的mAP@50.
- 显示平均推断时间为9.52ms (≈12fps),使实时处理成为可能.
- 在多个基准数据集 (印度,TT100k,GTSDB) 上,与最先进的模型相比,表现得更好.
结论:
- 拟议的混合YOLO-ViT模型有效地解决了实时微小交通标志检测的挑战.
- 地图集成和混合架构显著降低了计算负担和错误阳性.
- 该框架在多样化和具有挑战性的道路环境中表现出卓越的效率和准确性.
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