对于连续交通场景的半监控车道检测
Liwei Deng1, He Cao1, Qingbo Dong1
1School of Automation, Harbin University of Science and Technology, Harbin, China.
Traffic injury prevention
|June 15, 2023
概括
这项研究引入了一种新的视频级车道检测算法,用于自动驾驶. 多ERFNet-ConvLSTM模型在复杂的交通场景和不同的速度中提高了准确性和效率.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 进步的自动驾驶技术需要强大的车道检测.
- 当前的图像级算法在动态,现实世界的交通场景中面临局限性.
研究的目的:
- 升级车道检测从图像到视频水平,以增强自动驾驶.
- 提出一个具有成本效益的算法,能够处理复杂的交通和使用连续图像输入的各种驾驶速度.
主要方法:
- 推出多ERFNet-ConvLSTM网络框架,集成高效剩余因子化的ConvNet (ERFNet) 和卷积长短期内存 (ConvLSTM).
- 纳入Pyramidally Attended Feature Extraction (PAFE) 模块来管理多尺度的车道对象.
- 使用分割数据集进行评估,并进行全面的多维评估.
主要成果:
- 多ERFNet-ConvLSTM算法在准确性,精度和F1得分方面超过了基准方法.
- 在复杂的交通场景中表现出卓越的检测能力和在不同驾驶速度的有效性能.
结论:
- 多ERFNet-ConvLSTM算法为自动驾驶系统中的视频级车道检测提供了一个强大的解决方案.
- 高性能,降低标签成本和适应各种条件的适应性使其适合于现实世界的应用.
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