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YoloDepth: Yolo with Monocular Depth Estimation for Object Distance Measurement
Chen Fei-Yang,Jiao Ji-Chao *
School of Electronic Engineering, Beijing University of Posts adn Telecommunications, Beijing 100876
*Correspondence author
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Funding: none
Opened online:17 February 2023
Accepted by: none
Citation: Chen Fei-Yang,Jiao Ji-Chao.YoloDepth: Yolo with Monocular Depth Estimation for Object Distance Measurement[OL]. [17 February 2023] http://en.paper.edu.cn/en_releasepaper/content/4759099
 
 
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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