Wedge angle and orientation recognition of multi-opening objects using an attention-based CNN model Article Swipe
Yiwen Zhang
,
Siao Li
,
Xiaoyan Wang
,
Yongxiong Ren
,
Zihan Geng
,
Fei Yang
,
Zhongqi Pan
,
Yang Yue
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1364/oe.529655
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1364/oe.529655
In industries such as manufacturing and safety monitoring, accurately identifying the shape characteristics of multi-opening objects is essential for the assembly, maintenance, and fault diagnosis of machinery components. Compared to traditional contact sensing methods, image-based feature recognition technology offers non-destructive assessment and greater efficiency, holding significant practical value in these fields. Although convolutional neural networks (CNNs) have achieved remarkable success in image classification and feature recognition tasks, they still face challenges in dealing with subtle features in complex backgrounds, especially for objects with similar openings, where minute angle differences are critical. To improve the identification accuracy and speed, this study introduces an efficient CNN model, ADSA-Net, which utilizes the additive self-attention mechanism. When coupled with an active light source system, ADSA-Net enables non-contact, high-precision recognition of shape features in 14 classes of rotationally symmetric objects with multiple openings. Experimental results demonstrate that ADSA-Net achieves accuracies of 100%, ≥98.04%, and ≥98.98% in identifying the number of openings, wedge angles, and opening orientations of all objects, respectively with a resolution of 1°. By adopting linear layers to replace the traditional quadratic matrix multiplication operations for key-value interactions, ADSA-Net significantly enhances computational efficiency and identification accuracy.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1364/oe.529655
- OA Status
- gold
- Cited By
- 3
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400742126
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4400742126Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1364/oe.529655Digital Object Identifier
- Title
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Wedge angle and orientation recognition of multi-opening objects using an attention-based CNN modelWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-07-17Full publication date if available
- Authors
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Yiwen Zhang, Siao Li, Xiaoyan Wang, Yongxiong Ren, Zihan Geng, Fei Yang, Zhongqi Pan, Yang YueList of authors in order
- Landing page
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https://doi.org/10.1364/oe.529655Publisher landing page
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1364/oe.529655Direct OA link when available
- Concepts
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Computer science, Artificial intelligence, Wedge (geometry), Convolutional neural network, Computer vision, Pattern recognition (psychology), Feature (linguistics), Identification (biology), Orientation (vector space), Mathematics, Geometry, Botany, Biology, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| publication_date | 2024-07-17 |
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