走向更小更强:在水上场景中为无人驾驶地面车辆采用边缘意识的轻量级细分方法
Wei Han1,2, Binyu Zhao3, Jun Luo1
1School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
一种新的轻量级方法ELNet (边缘感知轻量级水场景细分) 改善了无人驾驶车辆的实时表面检测. 它在嵌入式系统上实现了高精度和稳定性,优于现有模型.
科学领域:
- 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
- 人工智能的人工智能
背景情况:
- 准确的地表区域检测对于地表无人驾驶汽车系统至关重要.
- 现有的方法往往缺乏嵌入式设备所需的轻量级和实时性能.
研究的目的:
- 提出适合嵌入式应用的边缘感知轻量级水场景细分方法 (ELNet).
- 为了提高水场景中表面检测的实时性能和准确性.
主要方法:
- ELNet采用双流学习方法,扩展空间细节和前端信息.
- 该方法整合了空间和上下文流,增强了像素级视觉建模,而不增加推理计算.
主要成果:
- 在MODS基准上,ELNet实现了45.21%的FPS,98.5%的检测稳定性和75.1%的F-score.
- 在USV内陆数据集上,ELNet显示了97.82%的精度和93.96%的F-score.
- 该方法使用较少的参数,同时保持可比的准确性和卓越的实时性能.
结论:
- 在嵌入式系统上,ELNet为轻量级和实时水场景细分提供了有效的解决方案.
- 拟议的方法提高了水面无人驾驶车辆系统在各种水环境中的实际适用性.
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