论文标题
R-TOD：自动驾驶的端到端端延迟的实时对象检测器
R-TOD: Real-Time Object Detector with Minimized End-to-End Delay for Autonomous Driving
论文作者
论文摘要
为了实现安全的自主驾驶，应彻底分析和最小化实时对象检测系统的端到端延迟。但是，尽管最近开发了最小的推理延迟神经网络，但令人惊讶的是，在报道发现其检测之前，很少关注其端到端的延迟。有了这一动机，本文旨在提供对端到端延迟的更全面的理解，通过这些延迟进行了精确的最佳和最差案例延迟预测，并实施了三种优化方法：（i）按需捕获，（ii）零斜线管道和（iii）无争议的管道。我们的实验结果表明，Darknet Yolo的端到端延迟降低了76％（您只看一次）V3（从1070 ms到261 ms），从而证明了利用自动驾驶的端到端延迟分析的巨大潜力。此外，由于我们仅修改系统体系结构并不会改变神经网络体系结构本身，因此我们的方法不会对检测准确性受到惩罚。
For realizing safe autonomous driving, the end-to-end delays of real-time object detection systems should be thoroughly analyzed and minimized. However, despite recent development of neural networks with minimized inference delays, surprisingly little attention has been paid to their end-to-end delays from an object's appearance until its detection is reported. With this motivation, this paper aims to provide more comprehensive understanding of the end-to-end delay, through which precise best- and worst-case delay predictions are formulated, and three optimization methods are implemented: (i) on-demand capture, (ii) zero-slack pipeline, and (iii) contention-free pipeline. Our experimental results show a 76% reduction in the end-to-end delay of Darknet YOLO (You Only Look Once) v3 (from 1070 ms to 261 ms), thereby demonstrating the great potential of exploiting the end-to-end delay analysis for autonomous driving. Furthermore, as we only modify the system architecture and do not change the neural network architecture itself, our approach incurs no penalty on the detection accuracy.
