A Real-Time Defect Detection Method for Digital Signal Processing of 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 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/AP(50) of 41.8/80.2 at 33 fps on NEU-DET, which satisfies the requirement of real-time systems.

Automated summary

This article introduces a novel defect detection method based on deep learning for digital signal processing of industrial inspection applications. By utilizing a feature collection and compression network and a Gaussian weighted pooling method, the proposed method improves both accuracy and efficiency in real-time systems, achieving mAP/AP(50) of 41.8/80.2 at 33 fps on NEU-DET.