论文标题
自动驾驶应用程序的自动稳定实时检测学习
Self-Configurable Stabilized Real-Time Detection Learning for Autonomous Driving Applications
论文作者
论文摘要
在自主驾驶环境中，确保实时和准确的对象检测至关重要。但是，现有的对象检测神经网络系统的特征是计算时间和准确性之间的权衡，因此必须优化这种权衡。幸运的是，在许多自动驾驶环境中，图像以连续的形式出现，提供了使用光流的机会。在本文中，我们利用光流估计来提高对象检测神经网络的性能。此外，我们为稳定性前提，为时间平均的性能最大化提出了一个Lyapunov优化框架。它可以自适应地确定是否使用光流程适合动态车辆环境，从而确保车辆的队列稳定性和同时的时间平均最高性能。为了验证关键思想，我们使用各种对象检测神经网络和光流估计网络进行数值实验。此外，我们通过Yolov3-tiny和Flownet2-S演示了可自配置的稳定检测，它们分别是实时对象检测网络和光流估计网络。在演示中，我们提出的框架将准确性提高了3.02％，检测到的对象数量增加了59.6％，并且用于计算功能的队列稳定性。
Guaranteeing real-time and accurate object detection simultaneously is paramount in autonomous driving environments. However, the existing object detection neural network systems are characterized by a tradeoff between computation time and accuracy, making it essential to optimize such a tradeoff. Fortunately, in many autonomous driving environments, images come in a continuous form, providing an opportunity to use optical flow. In this paper, we improve the performance of an object detection neural network utilizing optical flow estimation. In addition, we propose a Lyapunov optimization framework for time-average performance maximization subject to stability. It adaptively determines whether to use optical flow to suit the dynamic vehicle environment, thereby ensuring the vehicle's queue stability and the time-average maximum performance simultaneously. To verify the key ideas, we conduct numerical experiments with various object detection neural networks and optical flow estimation networks. In addition, we demonstrate the self-configurable stabilized detection with YOLOv3-tiny and FlowNet2-S, which are the real-time object detection network and an optical flow estimation network, respectively. In the demonstration, our proposed framework improves the accuracy by 3.02%, the number of detected objects by 59.6%, and the queue stability for computing capabilities.
