在YOLOv5s中使用ML_SAP增强小目标交通标志检测
Zhenguo Lu1, Zhibo Zhu1, Weipeng Xu2,3
1College of Transportation, Shandong University of Science and Technology, Qingdao, 266590, Shandong Province, People's Republic of China.
Scientific reports
|October 30, 2024
概括
这项研究引入了一个增强的YOLOv5s模型,具有多层压缩特征感知 (ML_SAP) 机制,用于改进小型交通标志检测. 新模型在复杂的交通场景中显著提高了准确性和回忆力.
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:
- 由于复杂和动态的现实世界条件,检测小型交通标志是很困难的.
- 现有的模型经常在交通标志检测的规模和变化方面扎.
研究的目的:
- 为了提高小型交通标志检测的准确性和可靠性.
- 为了提高YOLOv5s模型在充满挑战的交通环境中的性能.
主要方法:
- 开发了一种改进的YOLOv5s模型,其中包含了针对小目标的额外检测层.
- 采用了低损失函数来优化箱质量.
- 引入了一种多层压缩特征感知 (ML_SAP) 机制,用于增强特征融合和提取.
主要成果:
- 与YOLOv5s.相比,改进的模型在CCTSDB-2021数据集上实现了5.4%更高的检测回忆 (77.1%) 和3.9%更高的mAP (82.7%),相比于YOLOv5s.
- 在TT100K数据集上,该模型显示的召回率比基础YOLOv5s模型高3.7% (91.3%),mAP高4.6% (91.5%).
- ML_SAP机制提高了模型在各种条件下检测小目标的能力.
结论:
- 拟议的增强型YOLOv5s模型与ML_SAP显著提高了小型交通标志检测的准确性.
- 该模型在公共数据集上表现出卓越的性能,为智能运输系统提供了更强大的解决方案.
- 集成高级功能和丢失功能解决了现实世界交通标志识别的关键挑战.
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