Mobip:使用MobileNet进行驾驶感知的一种轻型模型.
1School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, China.
Frontiers in neurorobotics
|December 19, 2023
概括
我们开发了Mobip,这是一个快速,轻量级的多任务网络,用于自动驾驶感知. 它有效地处理对象检测,可驾驶区域细分和车道线路检测,这对于自动驾驶汽车的决策至关重要.
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:
- 视觉感知模型对于自动驾驶系统至关重要,使自动驾驶汽车能够解释交通场景.
- 准确和高效的感知是复杂的驾驶环境中安全导航和决策的关键.
研究的目的:
- 提出一个名为Mobip的轻量级多任务网络,用于同时检测交通对象,可驾驶区域细分和车道线路检测.
- 在不影响关键感知任务的性能的情况下实现高推断速度.
主要方法:
- 开发了一种多任务网络 (Mobip),具有共享编码器 (MobileNetV2骨干) 和两个解码器,用于联合检测和细分.
- 实现了高效的多任务架构,以优化功能提取和任务特定处理.
主要成果:
- 在NVIDIA特斯拉V100 GPU上,Mobip实现了58 FPS的推理速度.
- 该模型在BDD100K数据集上显示了对象检测,可驾驶区域细分和车道线路检测方面的竞争性性能.
- 废弃性研究证实了拟议的多任务架构的有效性和效率.
结论:
- 轻量级的Mobip网络为自动驾驶中的多任务视觉感知提供了高效的解决方案.
- 拟议的架构平衡了推断速度和性能,使其适合实时应用.
- Mobip提供了一种可行的方法来提高自动驾驶汽车的决策能力.
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