research-article

Automatic Defect Recognition Method of Aluminium Profile Surface Defects

Authors:

Jianyong LiAuthors Info & Claims

ICRAI '21: Proceedings of the 7th International Conference on Robotics and Artificial Intelligence

Pages 21 - 25

https://doi.org/10.1145/3505688.3505692

Published: 09 April 2022 Publication History

Abstract

Automatic defect detection has important implications to intelligent manufacturing which could be used for the precise quality control of different products. However, the diverse aluminium profile surface defects present the characteristics of micro defects and different sizes. Conventional handcrafted-based methods and machine learning-based methods have limited feature expression ability which cause relatively poor detection performance. Recently, with the stronger feature extraction ability, deep learning has got wide applications on defect detection and recognition. Due to the loss information caused by pooling operations, it still exists a certain drawbacks on multi-scale object detection. To address this issue, with the residual neural network (ResNet), a new deep defect recognition network is proposed in this paper for aluminium profile surface defects to construct an end-to-end defect detection scheme. An attention fusion model is proposed to improve the detection precision on multi-scale defects. Experiments show that the proposed defect detection method shows a better detection performance compared with other advanced detection models.

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Cited By

Shao YFan SZhao QZhang LSun H(2024)Surface Defect Detection of Aluminum Profiles Based on Multiscale and Self-Attention MechanismsSensors10.3390/s2409291424:9(2914)Online publication date: 2-May-2024
https://doi.org/10.3390/s24092914
Wang YWei YWu ZHe ZWang KDing ZZou L(2023)Adaptive convolutional neural network for aluminum surface defect detectionComputational Materials Science10.1016/j.commatsci.2023.112262227(112262)Online publication date: Aug-2023
https://doi.org/10.1016/j.commatsci.2023.112262

Index Terms

Automatic Defect Recognition Method of Aluminium Profile Surface Defects

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cover image ACM Other conferences

ICRAI '21: Proceedings of the 7th International Conference on Robotics and Artificial Intelligence

November 2021

135 pages

ISBN:9781450385855

DOI:10.1145/3505688

Copyright © 2021 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 [email protected].

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Publication History

Published: 09 April 2022

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National Natural Science Foundation of China
National Key Research & Development Project of China

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ICRAI 2021

ICRAI 2021: 2021 7th International Conference on Robotics and Artificial Intelligence

November 19 - 22, 2021

Guangzhou, China

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Cited By

Shao YFan SZhao QZhang LSun H(2024)Surface Defect Detection of Aluminum Profiles Based on Multiscale and Self-Attention MechanismsSensors10.3390/s2409291424:9(2914)Online publication date: 2-May-2024
https://doi.org/10.3390/s24092914
Wang YWei YWu ZHe ZWang KDing ZZou L(2023)Adaptive convolutional neural network for aluminum surface defect detectionComputational Materials Science10.1016/j.commatsci.2023.112262227(112262)Online publication date: Aug-2023
https://doi.org/10.1016/j.commatsci.2023.112262

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