LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

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

Photo from wikipedia

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… Click to show full 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/$\text{AP}_{50}$ of 41.8/80.2 at 33 fps on NEU-DET, which satisfies the requirement of real-time systems.

Keywords: defect detection; industrial inspection; digital signal; signal processing; processing industrial

Journal Title: IEEE Transactions on Industrial Informatics
Year Published: 2021

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.