Anytime-Lidar: Deadline-aware 3D Object Detection
Anytime-Lidar: Deadline-aware 3D Object Detection
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DOI：
10.1109/rtcsa55878.2022.00010
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Ahmet Soyyigit;Shuochao Yao;H. Yun
中科院分区：
文献类型：
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作者：
Ahmet Soyyigit;Shuochao Yao;H. Yun
在这项工作中，我们提出了一种新的调度框架--基于深度神经网络(DNN)的三维目标检测流水线的随时随地感知。我们主要针对3D目标检测流水线中常见的区域建议网络(RPN)和每个类别的多头检测器组件，并使它们具有截止期感知能力。提出了一种动态选择部件子集的调度算法，在有效的时间和精度之间进行动态的权衡。我们通过估计将先前检测到的对象投影到当前场景上，从而最大限度地减少跳过一些神经网络子组件的精度损失。我们将我们的方法应用于一个最先进的3D目标检测网络PointPillars，并使用nuScenes数据集在Jetson Xavier AGX上评估了它的性能。与基线相比，在不同的截止期约束下，我们的方法显著提高了网络的精度。
In this work, we present a novel scheduling frame-work enabling anytime perception for deep neural network (DNN) based 3D object detection pipelines. We focus on computationally expensive region proposal network (RPN) and per-category multi-head detector components, which are common in 3D object detection pipelines, and make them deadline-aware. We propose a scheduling algorithm, which intelligently selects the subset of the components to make effective time and accuracy trade-off on the fly. We minimize accuracy loss of skipping some of the neural network sub-components by projecting previously detected objects onto the current scene through estimations. We apply our approach to a state-of-art 3D object detection network, PointPillars, and evaluate its performance on Jetson Xavier AGX using nuScenes dataset. Compared to the baselines, our approach significantly improve the network’s accuracy under various deadline constraints.