Multimedia Tools and Applications

, Volume 78, Issue 1, pp 1053–1066 | Cite as

Segmentation of shallow scratches image using an improved multi-scale line detection approach

  • Xiaoliang JiangEmail author
  • Xiaojun Yang
  • Zhengen Ying
  • Liwen Zhang
  • Jie Pan
  • Shaojie Chen


Along with developing modern technology, the demands for optical element surface develop towards the characteristics of large scale and high precision. However, it is challenging to evaluate the surface defects since some shallow scratches in optical element surface images are usually characterized by low contrast and blurry outlines. This property makes the machine vision inspection extremely difficult. So, this paper proposes a novel multi-scale line detection method that can efficiently extract shallow scratches. Firstly, to decrease the influence of the surrounding region, a new multi-scale line detector combines all the responses at different scales by setting different weights for each scale. Then, based on the scratches features, we utilize morphological operations to get the full continuum of the scratches area. Experimental results show that our model can ideally extract the contours of shallow scratches that are very close to the optical microscope results observed by specialists.


Optical element Shallow scratches Multi-scale line detection Morphological operations 



The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve this paper. Besides, this work is supported by the National Natural Science Foundation of China (No. 51275272, 51605252, 51605253), Zhejiang Provincial Natural Science Foundation of China (No. LQ17C160001, LQ18F010007), Provincial public-benefit technology application research of Zhejiang (No. 2016C31127), Key Laboratory of Air-driven Equipment Technology of Zhejiang Province (No. 2018E10011).

Compliance with Ethical Standards

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this article.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Xiaoliang Jiang
    • 1
    • 2
    Email author
  • Xiaojun Yang
    • 1
  • Zhengen Ying
    • 1
  • Liwen Zhang
    • 1
  • Jie Pan
    • 1
  • Shaojie Chen
    • 2
  1. 1.College of Mechanical EngineeringQuzhou UniversityQuzhouChina
  2. 2.College of Mechanical EngineeringSouthwest Jiaotong UniversityChengduChina

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