| Item type |
文献 / Documents(1) |
| 公開日 |
2023-04-25 |
| アクセス権 |
|
|
アクセス権 |
open access |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 出版社版DOI |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.3390/met12111809 |
|
|
言語 |
ja |
|
|
関連名称 |
10.3390/met12111809 |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| タイトル |
|
|
タイトル |
An Effective Surface Defect Classification Method Based on RepVGG with CBAM Attention Mechanism (RepVGG-CBAM) for Aluminum Profiles |
|
言語 |
en |
| 著者 |
Li, Zhiyang
Li, Bin
Ni, Hongjun
任, 福継
Lv, Shuaishuai
康, 鑫
|
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
The automatic classification of aluminum profile surface defects is of great significance in improving the surface quality of aluminum profiles in practical production. This classification is influenced by the small and unbalanced number of samples and lack of uniformity in the size and spatial distribution of aluminum profile surface defects. It is difficult to achieve high classification accuracy by directly using the current advanced classification algorithms. In this paper, digital image processing methods such as rotation, flipping, contrast, and luminance transformation were used to augment the number of samples and imitate the complex imaging environment in actual practice. A RepVGG with CBAM attention mechanism (RepVGG-CBAM) model was proposed and applied to classify ten types of aluminum profile surface defects. The classification accuracy reached 99.41%, in particular, the proposed method can perfectly classify six types of defects: concave line (cl), exposed bottom (eb), exposed corner bottom (ecb), mixed color (mc), non-conductivity (nc) and orange peel (op), with 100% precision, recall, and F1. Compared with the existing advanced classification algorithms VGG16, VGG19, ResNet34, ResNet50, ShuffleNet_v2, and basic RepVGG, our model is the best in terms of accuracy, macro precision, macro recall and macro F1, and the accuracy was improved by 4.85% over basic RepVGG. Finally, an ablation experiment proved that the classification ability was strongest when the CBAM attention mechanism was added following Stage 1 to Stage 4 of RepVGG. Overall, the method we proposed in this paper has a significant reference value for classifying aluminum profile surface defects. |
|
言語 |
en |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
aluminum profile |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
surface defect classification |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
RepVGG |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
CBAM |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
attention mechanism |
| 書誌情報 |
en : Metals
巻 12,
号 11,
p. 1809,
発行日 2022-10-25
|
| 収録物ID |
|
|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
20754701 |
| 出版者 |
|
|
出版者 |
MDPI |
|
言語 |
en |
| 権利情報 |
|
|
言語 |
en |
|
権利情報 |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
| EID |
|
|
識別子 |
394663 |
|
識別子タイプ |
URI |
| 言語 |
|
|
言語 |
eng |
met_12_11_1809.pdf (2.64 MB)
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