research-article
Authors: Kailun Bian, Guo Chen, Guoqing Xie, Juntong Li, Bocheng Liu
ASENS '24: Proceedings of the International Conference on Algorithms, Software Engineering, and Network Security
Pages 638 - 644
Published: 03 August 2024 Publication History
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Abstract
In recent years, with the advancement of deep learning technology, the task of industrial component defect detection has shifted from manual inspection to deep learning model detection. However, striking a balance between the precision and speed required by industrial production has become a new challenge. This paper categorizes the current mainstream object detection algorithms into three types: one-stage detection algorithms, two-stage detection algorithms, and transformer-based detection algorithms. The structures and characteristics of each type of algorithm are elucidated. Comparative experimental studies are conducted to analyze the advantages and disadvantages of these algorithms. The paper summarizes optimization methods and effects for each type of algorithm and offers a forward-looking perspective on the prospective trends in the evolution of defect detection algorithms.
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Index Terms
Industrial Component Defect Detection Technology Based on Deep Learning
Computing methodologies
Artificial intelligence
Computer vision
Computer vision problems
Object detection
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Published In
ASENS '24: Proceedings of the International Conference on Algorithms, Software Engineering, and Network Security
April 2024
759 pages
ISBN:9798400709784
DOI:10.1145/3677182
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [emailprotected].
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 03 August 2024
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ASENS 2024
ASENS 2024: International Conference on Algorithms, Software Engineering, and Network Security
April 26 - 28, 2024
Nanchang, China
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