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Surface weak scratch detection for optical elements based on a multimodal imaging system and a deep encoder-decoder

Xiao Liang, Jingshuang Sun, Xuewei Wang

Journal of the Optical Society of America. A, Optics, Image Science, and Vision

|September 14, 2023

Summary

This study introduces a new imaging system and deep learning model for detecting faint surface scratches, improving clarity and accuracy in optical industries. The combined approach enhances scratch detection performance with fewer computational resources.

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Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization

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Published on: July 5, 2016

Area of Science:

Optics and Materials Science
Computer Vision and Machine Learning

Background:

Detecting subtle surface scratches in optics is challenging due to their small dimensions.
Existing methods struggle with clarity and blur caused by light scattering.

Purpose of the Study:

To develop an effective system for detecting weak surface scratches.
To improve image quality and segmentation accuracy for scratch detection.

Main Methods:

A multimodal microscopic imaging system combining discrete multispectral illumination and linear polarization.
A novel U-shaped deep encoder-decoder network with specialized modules for scratch segmentation.

Main Results:

The imaging system enhanced scratch visibility and image clarity.
The deep learning model accurately segmented weak scratches with high performance and fewer parameters.
The model demonstrated strong generalization on a building crack dataset.

Conclusions:

The integrated imaging and deep learning approach offers a robust solution for weak scratch detection.
This method significantly advances the state-of-the-art in optical surface inspection.