<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">JERA</journal-id><journal-title-group><journal-title>Journal of Electronic Research and Application</journal-title></journal-title-group><issn>2208-3502</issn><eissn>2208-3510</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/jera.v10i3.14662</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Practical Application of Low-Cost Visual Inspection Systems in Industrial Robot Integration</title><url>https://artdesignp.com/journal/JERA/10/3/10.26689/jera.v10i3.14662</url><author>QiChang</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>10</volume><issue>3</issue><history><date date-type="pub"><published-time>2026-04-24</published-time></date></history><abstract>In the transformation of industrial automation to smart manufacturing, visual inspection systems as critical sensing technologies are hindered by their high costs and algorithmic complexity, impeding the intelligent upgrading of small and medium-sized enterprises. This study focuses on low-cost visual inspection systems, enhancing performance through the selection of domestic industrial cameras, optimization of OpenCV and lightweight deep learning model algorithms, and the use of a C++ parallel computing framework, thereby constructing a solution that balances accuracy and cost. Experiments demonstrate that the system achieves sub-millimeter-level positioning and highly reliable detection in scenarios such as assembly guidance and defect identification, significantly reducing hardware costs while maintaining millisecond-level response capabilities, providing a feasible path for the intelligent upgrading of small and medium-sized enterprises.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Li B, 2021, Research on Industrial Image Annotation Method Based on Deep Learning, thesis, Huazhong University of Science and Technology.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B2" content-type="article"><label>2</label><element-citation publication-type="journal"><p>Wei R, 2025, Optimization Strategy for Industrial Robot Vision Inspection System Based on Artificial Intelligence. Science and Technology Vision, 15(31): 9–11.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B3" content-type="article"><label>3</label><element-citation publication-type="journal"><p>Wang X, 2024, Design of Industrial Robot Vision Inspection System. Mold Manufacturing, 24(10): 215–217.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B4" content-type="article"><label>4</label><element-citation publication-type="journal"><p>Zhou S, 2025, Research and Design of an Online Defect Detection Device for Differential Housing, thesis, Dalian Jiaotong University.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B5" content-type="article"><label>5</label><element-citation publication-type="journal"><p>Chen J, 2025, Design and Implementation of Visual Inspection Algorithm for Fiber Optic Distribution Equipment, thesis, Beijing University of Posts and Telecommunications.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
