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[期刊论文]

Measuring the absolute distance of a front vehicle from an in-car camera based on monocular vision and instance segmentation

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author:

Huang, Liqin (Huang, Liqin.) [1] (Scholars:黄立勤) | Chen, Yanan (Chen, Yanan.) [2] | Fan, Zhengjia (Fan, Zhengjia.) [3] | Unfold

Indexed by:

EI Scopus SCIE

Abstract:

The requirements of distance measurement have increased with the development of auto vehicle driving. Traditional methods for distance estimation require the complex calibration from intrinsic and external parameters of the camera. Recent methods based on the neural network structure mainly measure the relative depth of whole images. We adopt monocular vision with instance segmentation and camera focal length to detect the absolute distance of front cars from in-car cameras. First, we extract the location of the cars from the object detection network. Second, the location of cars is sent to the vehicle classification network and instance segmentation network to obtain the type of the cars and their mask value. Here, we use a model trained by the CompCars dataset to classify car types, and we train a new instance segmentation model using the Cityscapes dataset to obtain each car's mask. Third, in accordance with the camera imaging principle, the absolute distance of cars in the images is calculated based on the relationship between the size information of different car types and their mask values. The proposed method is examined with the KITTI dataset, and the experiment shows that its results can be close to the ground truth. Moreover, the proposed method uses the instance segmentation network to reduce complexity of the depth estimation process and it can still generate satisfactory results even when cars are partly occluded. (C) 2018 SPIE and IS&T

Keyword:

absolute distance estimation deep learning instance segmentation monocular vision vehicle classification

Community:

  • [ 1 ] [Huang, Liqin]Fuzhou Univ, Coll Phys & Informat, Fuzhou, Fujian, Peoples R China
  • [ 2 ] [Chen, Yanan]Fuzhou Univ, Coll Phys & Informat, Fuzhou, Fujian, Peoples R China
  • [ 3 ] [Fan, Zhengjia]Fuzhou Univ, Coll Phys & Informat, Fuzhou, Fujian, Peoples R China
  • [ 4 ] [Chen, Zhifeng]Fuzhou Univ, Coll Phys & Informat, Fuzhou, Fujian, Peoples R China

Reprint 's Address:

  • 陈志峰

    [Chen, Zhifeng]Fuzhou Univ, Coll Phys & Informat, Fuzhou, Fujian, Peoples R China

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Source :

JOURNAL OF ELECTRONIC IMAGING

ISSN: 1017-9909

Year: 2018

Issue: 4

Volume: 27

0 . 9 2 4

JCR@2018

1 . 0 0 0

JCR@2023

ESI Discipline: ENGINEERING;

ESI HC Threshold:170

JCR Journal Grade:4

CAS Journal Grade:4

Cited Count:

WoS CC Cited Count: 11

SCOPUS Cited Count: 18

30 Days PV: 0

操作日志

管理员  2025-02-15 23:49:20  更新被引

管理员  2025-02-15 23:49:20  更新被引

管理员  2020-11-12 23:42:19  创建

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