A Real-Time Defect Detection Method for Digital Signal Processing of Industrial Inspection Applications
Abstract
The signal processing of industrial big data (IBD) is a challenging task, owing to the complex working scenarios and the lack of annotations. Defect detection, which is an important subject of IBD research works, has shown its effectiveness in digital signal processing of industrial inspection applications in many previous studies. This article proposes a novel defect detection method based on deep learning for digital signal processing of industrial inspection applications. In our method, a module named feature collection and compression network is applied to merge multiscale feature information. Then, a new pooling method named Gaussian weighted pooling, which provides more precise location information, is used to replace region of interest (ROI) pooling. Experiment results show that our method gets improvements in both accuracy and efficiency, with mAP/AP50 of 41.8/80.2 at 33 fps on NEUDET, which satisfies the requirement of real-time systems.
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Deep learning-based methods have been proposed for automatic real-time visual defect inspection.
“Recently, deep learning-based methods have been proposed for automatic real-time visual defect inspection [264].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [264]
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Cites
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works
An End‐to‐End Steel Strip Surface Defects Recognition System Based on Convolutional Neural Networks
Yi, L., Li, G., & Jiang, M. (2017) · steel research international · doi:10.1002/srin.201600068 · Cited by 173 · cited 1× · see citing works
“With high-level semantic features, the deep learning based methods [11], [12] are able to get higher accuracies.”
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Zhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774 · see citing works
- Authors
- Gao, Y., Lin, J., Xie, J., & Ning, Z.
- Affiliations
- South China University of Technology (CN)
- Chongqing University of Posts and Telecommunications (CN)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2021-05
- Volume
- 17
- Issue
- 5
- Pages
- 3450-3459
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W3046756309
- Cited by
- 82
- References
- 3
- Citations by year
- 2026: 9 · 2025: 23 · 2024: 17 · 2023: 17 · 2022: 12 · 2021: 3 · 2020: 1
- FWCI
- 5.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- Funders
- Key R&D Program in Key Areas of Guangdong Province (2019B010137001, 2020B010166001)
- Industrial Internet Innovation and Development (MIZ1824020)
- Guangzhou City Industrial Technology Major Research (201802010035)
- National Natural Science Foundation of China (cstc2019jcyj-msxmX0208)
- National Mobile Communications Research Laboratory, Southeast University
- Southeast University (2020D05)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
- Keywords
- defect detection, industrial inspection, industrial big data, real-time detection, multi-scale feature fusion
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