YoloDepth: Yolo with Monocular Depth Estimation for Object Distance Measurement
首发时间:2023-02-17
Abstract:Environmental perception system is an important part of autonomous driving. A high-precision, real-time perception system can help the vehicles make feasible decisions and reasonable plans for the next step while driving. We propose a multi-task environmental perception network (YoloDepth) that can simultaneously perform traffic object detection and distance measurement. It consists of an encoder for feature extraction and two decoders for specific tasks. Our model performs excellently on COCO 2017 object detection dataset and KITTI monocular depth estimation dataset, achieving state-of-the-art speed and accuracy, and can process both visual perception tasks simultaneously on the embedded device Jeston AGX Xavier (18.3 FPS) in real-time and maintain great accuracy.
keywords: environment perception deep learning target detection monocular depth estimation target distance measurement
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YoloDepth:基于目标检测和单目深度估计融合的目标距离测量网络
摘要:环境感知系统是自动驾驶的一个重要组成部分。高精度、实时的环境感知系统可以帮助无人车辆在行驶的过程中做出可行性决策,并进行下一步的合理规划。本文提出了一种可同时进行交通目标检测和目标距离测量的多任务环境感知网络(YoloDepth)。它由一个用于特征提取的编码器和两个用于处理特定任务的解码器组成。我们的模型在COCO 2017目标检测数据集和KITTI单目深度估计数据集上具有非常优异的表现,达到了先进的速度 (18.3 FPS)和精度,并可以在嵌入式设备Jeston AGX Xavier 同时实时处理这两个可视化感知任务,并保持良好的准确性。
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YoloDepth:基于目标检测和单目深度估计融合的目标距离测量网络
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