Self-Configurable Stabilized Real-Time Detection Learning for Autonomous Driving Applications

• URL: http://arxiv.org/abs/2209.14525v1
• Date: Thu, 29 Sep 2022 03:11:33 GMT
• Title: Self-Configurable Stabilized Real-Time Detection Learning for Autonomous Driving Applications
• Authors: Won Joon Yun, Soohyun Park, Joongheon Kim, David Mohaisen
• Abstract summary: We improve the performance of an object detection neural network utilizing optical flow estimation. It adaptively determines whether to use optical flow to suit the dynamic vehicle environment. 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.
• Score: 15.689145350449737
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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