On-machine Surface Defect Detection Using Light Scattering and Deep Learning

Mingyu LIU (Corresponding Author), Chi Fai Cheung, Nicola Senin, Shixiang Wang, Rong So, Richard Leach

Research output: Journal article publicationJournal articleAcademic researchpeer-review

36 Citations (Scopus)


This paper presents an on-machine surface defect detection system using light scattering and deep learning. A supervised deep learning model is used to mine the information related to defects from light scattering patterns. A convolutional neural network is trained on a large dataset of scattering patterns that are predicted by a rigorous forward scattering model. The model is valid for any surface topography with homogeneous materials and has been verified by comparing with experimental data. Once the neural network is trained, it allows for fast, accurate, and robust defect detection. The system capability is validated on microstructured surfaces produced by ultraprecision diamond machining.
Original languageEnglish
Pages (from-to)B53-B59
Number of pages7
JournalJournal of the Optical Society of America A: Optics and Image Science, and Vision
Issue number9
Publication statusPublished - Sept 2020

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Computer Vision and Pattern Recognition


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