A rapid recognition method for electronic components based on the improved yolo-v3 network
Open accessAbstract
Rapid object recognition in the industrial field is the key to intelligent manufacturing. The research on fast recognition methods based on deep learning was the focus of researchers in recent years, but the balance between detection speed and accuracy was not well solved. In this paper, a fast recognition method for electronic components in a complex background is presented. Firstly, we built the image dataset, including image acquisition, image augmentation, and image labeling. Secondly, a fast recognition method based on deep learning was proposed. The balance between detection accuracy and detection speed was solved through the lightweight improvement of YOLO (You Only Look Once)-V3 network model. Finally, the experiment was completed, and the proposed method was compared with several popular detection methods. The results showed that the accuracy reached 95.21% and the speed was 0.0794 s, which proved the superiority of this method for electronic component detection.
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Data augmentation techniques show a general improvement in accuracy in every neural network framework.
“As mentioned in their paper [30], data augmentation techniques show a general improvement in accuracy in every neural network framework.”
From A review on modern defect detection models using DCNNs – Deep convolutional neural networks (2022) · cites this paper as [30]
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A review on modern defect detection models using DCNNs – Deep convolutional neural networks
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Self-citation
- Authors
- Huang, R., Gu, J., Sun, X., Hou, Y., & Uddin, S.
- Affiliations
- Jiangsu University (CN)
- Corresponding author
- Gu, Jinan
- Hou, Yongtao
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2019-07-25
- Volume
- 8
- Issue
- 8
- Pages
- 825
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W2963253967
- Cited by
- 94
- References
- 30
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 14 · 2023: 20 · 2022: 22 · 2021: 16 · 2020: 6 · 2019: 2
- FWCI
- 5.14
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Electronics (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (No. 51875266)
- Jiangsu Province Graduate Research and Innovation Program (No. KYCX18-2227)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Keywords
- YOLOv3, lightweight object detection, industrial visual inspection, image dataset construction, image augmentation, smart manufacturing
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