无人车行驶环境图像的几何测距*

doi:10.19562/j.chinasae.qcgc.2020.08.006

Abstract

Abstract: A geometric ranging method for the image of the driving environment of the unmanned vehicle is proposed. Firstly, the migration-learning method is used to improve the Tiny-YOLOv2 network model so as to train and detect the object to be identified, mark the object and locate the position of the object in the image. Secondly, a method of object classification, edge detection and edge fitting is proposed to further extract image information of the object. Finally, a ranging model based on spatial geometry theory is established, and the distance measurement of the object is realized by combining the prior information of the object size. With this method, more than 88% of the measurement error within 4 m is less than 0.2 m, and the measurement error does not change much with the increase of distance.

Key words: geometric ranging, monocular vision, convolutional neural network, migration learning, image segmentation

Dai Jinkun, Luo Yutao, Liang Weiqiang. Geometric Ranging of Unmanned Vehicle Driving Environment Image[J].Automotive Engineering, 2020, 42(8): 1034-1039.

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Geometric Ranging of Unmanned Vehicle Driving Environment Image

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