为自动驾驶提供多任务学习的最佳配置
Woomin Jun1,2, Minjun Son1,2, Jisang Yoo1,2
1Electronic Engineering, Dong Seoul University, Seongnam 13117, Republic of Korea.
Sensors (Basel, Switzerland)
|December 23, 2023
概括
本研究介绍了一种用于自动驾驶的多任务学习 (MTL) 优化算法 (MDO),提高图像识别任务的准确性和减少延迟. MDO算法有效地选择和优化任务组合,提高整体系统性能.
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:
- 自动驾驶需要高精度,来自各种传感器的实时图像识别.
- 目前的硬件限制限制限制了同时处理复杂视觉任务的可行性.
- 多任务学习 (MTL) 为高效并行处理多个图像识别任务提供了解决方案.
研究的目的:
- 研究MTL用于优化在硬件限制下自动驾驶的图像识别任务.
- 提出一种新的多任务决策和优化 (MDO) 算法,以提高处理速度,准确性和内存效率.
- 分析跨任务相关性 (ITC) 以有效构建多任务集 (MTS).
主要方法:
- 开发了三个步骤的MDO算法:MTS选择,共享的骨干/子网训练和网络压缩.
- 嵌入式半监督学习 (SSL) 用于额外的性能提升.
- 基于对象检测mAP,车道检测准确度和延迟减少的评估性能.
主要成果:
- MDO算法有效地构建了具有高ITC的MTS,这对于集成精度至关重要.
- 拟议的MDO算法与SSL相结合,实现了显著的改进.
- 与以前的方法相比,在物体检测mAP中显示了12%的增加,在车道检测准确度上有15%的改进,以及27%的延迟减少.
结论:
- 在MTL中,集成精度性能非常依赖于任务间相关性 (ITC).
- MDO算法为优化MTL配置以实现自动驾驶提供了一个有效的框架.
- 提出的方法显著优于现有的多任务学习技术,为更高效的自主系统铺平了道路.
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