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A Multilevel Information Fusion-Based Deep Learning Method for Vision-Based Defect Recognition
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- Gao, Yiping (author)
- Huazhong Univ Sci & Technol, Sch Mech Sci & Engn, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Peoples R China.
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- Gao, Liang (author)
- Huazhong Univ Sci & Technol, Sch Mech Sci & Engn, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Peoples R China.
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- Li, Xinyu (author)
- Huazhong Univ Sci & Technol, Sch Mech Sci & Engn, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Peoples R China.
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- Huazhong Univ Sci & Technol, Sch Mech Sci & Engn, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Peoples R China Industriell produktion (creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2020
- 2020
- English.
- In: IEEE Transactions on Instrumentation and Measurement. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9456 .- 1557-9662. ; 69:7, s. 3980-3991
- Journal article (peer-reviewed)
Abstract
Subject headings
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- Vision-based defect recognition is an important technology to guarantee quality in modern manufacturing systems. Deep learning (DL) becomes a research hotspot in vision-based defect recognition due to outstanding performances. However, most of the DL methods require a large sample to learn the defect information. While in some real-world cases, it is difficult and costly for data collecting, and only a small sample is available. Generally, a small sample contains less information, which may mislead the DL models so that they cannot work as expected. Therefore, this requirement impedes the wide applications of DL in vision-based defect recognition. To overcome this problem, this article proposes a multilevel information fusion-based DL method for vision-based defect recognition. In the proposed method, a three-level Gaussian pyramid is introduced to generate multilevel information of the defect so that more information is available for model training. After the Gaussian pyramid, three VGG16 networks are built to learn the information and the outputs are fused for the final recognition result. The experimental results show that the proposed method can extract more useful information and achieve better performances on small-sample tasks, compared with the conventional DL methods and defect recognition methods. Furthermore, the analysis results of the robustness and response time also indicate that the proposed method is robust for the noise input, and it is fast for defect recognition, which takes 13.74 ms to handle a defect image.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Keyword
- Deep learning (DL)
- defect recognition
- multilevel information fusion
- small sample
Publication and Content Type
- ref (subject category)
- art (subject category)
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