A Real-Time Defect Detection Method for Digital Signal Processing of Industrial Inspection Applications

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 appli...

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Veröffentlicht in:IEEE transactions on industrial informatics Jg. 17; H. 5; S. 3450 - 3459
Hauptverfasser: Gao, Ying, Lin, Jiqiang, Xie, Jie, Ning, Zhaolong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.05.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Annotations
Convolution
Defect detection
Detectors
Digital signal processing
dilated convolution
Feature extraction
Head
industrial big data (IBD)
industrial inspection application
Inspection
Real time
Signal processing
Task analysis
ISSN:1551-3203, 1941-0050
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2020.3013277