A Method of Surface Defect Detection of Irregular Industrial Products Based on Machine Vision

Author:

Li Mengkun1ORCID,Jia Junying2ORCID,Lu Xin2ORCID,Zhang Yue1ORCID

Affiliation:

1. School of Management, Capital Normal University, Beijing/100089, China

2. Shenyang Fengchi Software Co. LTD, Shenyang/110167, China

Abstract

In recent years, the surface defect detection technology of irregular industrial products based on machine vision has been widely used in various industrial scenarios. This paper takes Bluetooth headsets as an example, proposes a Bluetooth headset surface defect detection algorithm based on machine vision to quickly and accurately detect defects on the headset surface. After analyzing the surface characteristics and defect types of Bluetooth headsets, we proposed a surface scratch detection algorithm and a surface glue-overflowed detection algorithm. The result of the experiment shows that the detection algorithm can detect the surface defect of Bluetooth headsets fast as well as effectively, and the accuracy of defect recognition reaches 98%. The experiment verifies the correctness of the theory analysis and detection algorithm; therefore, the detection algorithm can be used in the recognition and detection of surface defect of Bluetooth headsets.

Funder

Scientific Research Foundation of Beijing Municipal Education Commission

Publisher

Hindawi Limited

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems

Reference26 articles.

1. Automatic surface defect detection for mobile phone screen glass based on machine vision;J. Chuanxia;Applied Soft Computing,2016

2. Machine vision based automatic apparatus and method for surface defect detection;X. Zhou

3. Rubber hose surface defect detection system based on machine vision

4. Wafer surface defect detection based on machine vision;C. Zhishan;Journal of Guizhou University(Natural Sciences),2019

5. Research on tile surface defect detection based on machine vision;X. Bo;Mechanical Engineering & Automation,2017

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