基于YOLOv5的改进,用于在复杂的天气条件下检测小型交通标志
Shenming Qu1, Xinyu Yang1, Huafei Zhou1
1School of Software, Henan University, Kaifeng, 475004, Henan, China.
Scientific reports
|September 27, 2023
概括
这项研究引入了一种改进的YOLOv5s模型,用于在复杂的天气条件下检测小型交通标志. 改进的算法实现了更高的精度和召回率,提高了自动驾驶的安全性.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 自主驾驶系统 自主驾驶系统
背景情况:
- 交通标志检测对于自动驾驶至关重要,但受到不同物体大小和天气条件的挑战.
- 现有的模型难以检测小型或封闭的交通标志,特别是在恶劣的天气中,这会影响整体检测准确度.
研究的目的:
- 开发一个改进的YOLOv5s算法,用于在复杂的天气条件下强大的交通标志检测.
- 为了提高小型和封闭的交通标志的检测精度和回忆.
主要方法:
- 在骨干中内置了协调注意力 (CA) 机制,以改善特征提取.
- 为YOLOv5s添加了一个预测头,以利用浅层的细粒度特征来检测小物体.
- 使用Alpha-IoU来提高边界框回归精度,而不是CIoU损失.
主要成果:
- 改进的YOLOv5s模型在CCTSDB 2021数据集上的小物体中实现了88.1%的精度和79.8%的回忆.
- 与原始YOLOv5s模型相比,在精度方面表现出12.5%的显著改进,在回忆方面表现出23.9%.
- 在不同的天气条件下有效地检测到小型交通标志,减少了错误率和高准确度.
结论:
- 拟议的算法有效地解决了在复杂的天气条件下检测小型交通标志的挑战.
- 集成协调注意力,额外的预测头和Alpha-IoU显著提高了检测性能.
- 这项工作通过改进的交通信号识别,有助于实现更安全,更可靠的自动驾驶系统.
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