Design the Abnormal Object Detection System Using Template Matching and Subtract Background Algorithm

Dang Thai Viet, Ngoc Tam Bui

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Computer vision is an artificial intelligence (AI) subfield that enables computers and systems to extract information from digital photos, movies, and other visual inputs. Detection systems are an important use in industrial production lines. In this paper, an automatic small abnormal object system is designed. First, the author obtains an image devoid of anomalous objects, which is then processed using the Candy filter to produce the standard form. Second, define the primary pattern, sub pattern 1, sub pattern 2, and the deviation between the new image and the original image. Then, we use template matching and background subtraction to identify questionable locations. Finally, live picture features will be compared to original image features. With Candy filter, the precision will be enhanced. The findings of image processing will be transmitted to operate the automatic abnormal removal equipment. The result indicates an accuracy of ~ 90%. The processing time is < 5 s, which has no effect on the production line cycle time.

Original languageEnglish
Title of host publicationProceedings of the 3rd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2022) - Volume 3
Subtitle of host publicationSustainable Approaches in Machine Design, Life Cycle Engineering, and Energy Management for Manufacturing Processes
EditorsBanh Tien Long, Kozo Ishizaki, Hyung Sun Kim, Yun-Hae Kim, Nguyen Duc Toan, Nguyen Thi Hong Minh, Pham Duc An
PublisherSpringer Science and Business Media Deutschland GmbH
Pages87-95
Number of pages9
ISBN (Print)9783031574597
DOIs
Publication statusPublished - 2024
Event3rd Annual International Conference on Material, Machines and Methods for Sustainable Development, MMMS 2022 - Can Tho, Viet Nam
Duration: 2022 Nov 102022 Nov 13

Publication series

NameLecture Notes in Mechanical Engineering
ISSN (Print)2195-4356
ISSN (Electronic)2195-4364

Conference

Conference3rd Annual International Conference on Material, Machines and Methods for Sustainable Development, MMMS 2022
Country/TerritoryViet Nam
CityCan Tho
Period22/11/1022/11/13

Keywords

  • Artificial intelligence
  • Computer vision
  • Detection system
  • Subtract background
  • Template matching

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Mechanical Engineering
  • Fluid Flow and Transfer Processes

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