CSW-YOLO:一个基于YOLOv8的交通标志小目标检测算法.
Qian Shen1,2, Yi Li1,2, YuXiang Zhang1,2
1School of Automation, Huaiyin Institute of Technology, Huaian, Jiangsu, China.
PloS one
|March 20, 2025
概括
本研究介绍了一种改进的YOLOv8算法,用于实时检测自动驾驶中的交通标志. 改进后的模型为复杂环境中的小目标识别提供了卓越的准确性和效率.
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:
- 实时交通标志检测对于自动驾驶安全至关重要.
- 现有的算法在复杂的交通场景中与小目标作斗争.
- YOLOv8提供了一个基础,但需要针对特定任务进行优化.
研究的目的:
- 为交通标志开发一个更有效,更准确的小目标检测算法.
- 增强YOLOv8模型,以提高自动驾驶中的实时性能.
- 为了降低计算负载,同时保持高检测精度.
主要方法:
- 修改了YOLOv8 C2f模块,使用了FasterNet的剩余Faster-Block和TransNeXt的CGLU (C2f-faster-CGLU).
- 集成大型可分离内核注意力 (LSKA) 与SPPF (SPPF-LSKA) 进行增强的特征提取.
- 包含一个专门的小目标检测层和WISE-Inner-MPDIoU损失函数.
主要成果:
- 在TT100K上获得Map50的89.8%,在CCTSDB 2021数据集上获得98.9%.
- 证明了与主流算法可比的精度.
- 显著减少模型参数和计算要求.
结论:
- 拟议的CSW-YOLO模型有效地改善了自动驾驶的交通标志检测.
- 这些改进带来了更高的准确性和效率,特别是在小型目标上.
- 该模型非常适合实时应用程序,其计算需求减少.
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