System Structural Error Analysis in Binocular Vision Measurement Systems

Author:

Yang Miao1,Qiu Yuquan2ORCID,Wang Xinyu2,Gu Jinwei3,Xiao Perry4ORCID

Affiliation:

1. Electronic Engineering Department, Jiangsu Ocean University, Lianyungang 222005, China

2. Marine Engineering Department, Jiangsu Ocean University, Lianyungang 222005, China

3. Ganyu Agricultural Development Group Co., Ltd., Lianyungang 222199, China

4. School of Engineering, London South Bank University, London SE1 0AA, UK

Abstract

A binocular stereo vision measurement system is widely used in fields such as industrial inspection and marine engineering due to its high accuracy, low cost, and ease of deployment. An unreasonable structural design can lead to difficulties in image matching and inaccuracies in depth computation during subsequent processing, thereby limiting the system’s performance and applicability. This paper establishes a systemic error analysis model to enable the validation of changes in structural parameters on the performance of the binocular vision measurement. Specifically, the impact of structural parameters such as baseline distance and object distance on measurement error is analyzed. Extensive experiments reveal that when the ratio of baseline length to object distance is between 1 and 1.5, and the angle between the baseline and the optical axis is between 30 and 40 degrees, the system measurement error is minimized. The experimental conclusions provide guidance for subsequent measurement system research and parameter design.

Funder

NSFC

National Key R&D Program Project

Key Country-Specific Industrial Technology R&D Cooperation Project

Jiangsu University of Science and Technology Marine Equipment Research Institute Project

Publisher

MDPI AG

Reference40 articles.

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3. Automated pixel-level pavement distress detection based on stereo vision and deep learning;Guan;Autom. Constr.,2021

4. Kahmen, O., Rofallski, R., and Luhmann, T. (2020). Impact of stereo camera calibration to object accuracy in multimedia photogrammetry. Remote Sens., 12.

5. Stereo matching algorithm based on deep learning: A survey;Hamid;J. King Saud Univ.-Comput. Inf. Sci.,2022

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