Automatic Positioning System for Industrial CT Image Defects Based on Machine Vision
Authors: 
Zaocai Xu, Yang Yang, Yi Ji, Dinggan ChengAuthors Info & Claims
Pages 235 - 239
Abstract
How to solve the low recall rate and poor positioning accuracy in current CT image defect positioning methods is a thorny problem. Therefore, this study proposes an automatic positioning system for industrial CT image defects based on machine vision. This article uses image preprocessing and scale-invariant feature transformation to process the image, uses a defect location algorithm to locate defects in CT images, and uses threshold segmentation and morphological operations to extract defect areas, and at the same time, designs experiments to compare and evaluate the positioning results with manual annotations, verifying the effectiveness and accuracy of the system. After experimental testing, the positioning accuracy of this system is between 0.01-0.1. The automatic industrial CT image defect positioning system based on machine vision can quickly and accurately locate different types of defects. Compared with traditional manual intervention methods, the system has higher efficiency and repeatability, which can improve the level of quality control on the production line.
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Index Terms
- Automatic Positioning System for Industrial CT Image Defects Based on Machine Vision
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Published In
December 2023
435 pages
ISBN:9798400716430
DOI:10.1145/3654446
Copyright © 2023 ACM.
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Association for Computing Machinery
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Publication History
Published: 03 May 2024
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SPCNC 2023
SPCNC 2023: The 2nd International Conference on Signal Processing, Computer Networks and Communications
December 8 - 10, 2023
Xiamen, China
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