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

• Published in: IEEE transactions on industrial informatics Vol. 17; no. 5; pp. 3450 - 3459
• Main Authors: Gao, Ying, Lin, Jiqiang, Xie, Jie, Ning, Zhaolong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.05.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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
• DOI: 10.1109/TII.2020.3013277

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.