基于改进的YOLOv5的自动驾驶快速准确的物体探测器
Xiang Jia1, Ying Tong2, Hongming Qiao2
1China Telecom Corporation Limited Beijing Research Institute, Beijing, China. jiax11@chinatelecom.cn.
Scientific reports
|June 15, 2023
概括
这项研究引入了一种改进的YOLOv5模型,用于在自动驾驶中更快,更准确地检测物体. 改进的探测器在KITTI数据集上实现了96.1%的准确性和202 FPS.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
背景情况:
- 实时物体检测对于自动驾驶汽车的安全性和稳定性至关重要.
- 现有的物体检测算法在实现自动驾驶应用的高精度和实时性能方面面临挑战.
研究的目的:
- 为自动驾驶系统提出一个快速而准确的物体检测模型.
- 提高YOLOv5算法的性能,以改善车辆和行人识别.
主要方法:
- 结构性重新参数化 (Rep) 用于解脱训练和推理,提高模型的速度和准确性.
- 通过神经架构搜索,优化了多分支重新参数化模块,提高了训练效率.
- 整合了小型物体检测层和协调注意力机制,以提高对行人和车辆等较小物体的检测.
主要成果:
- 建议改进的YOLOv5模型在KITTI数据集上实现了96.1%的检测准确度.
- 该模型显示了每秒202 (FPS) 的高推断速度.
- 改进后的模型在准确性和实时性能方面超过了几种主流的对象检测算法.
结论:
- 开发的物体检测方法显著提高了自动驾驶的准确性和实时能力.
- 整合Rep,NAS和注意力机制为先进的自动驾驶汽车感知系统提供了一个有希望的方法.
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