Surface defect detection method for glass substrate using improved Otsu segmentation

He, Zhiyong; Sun, Lining

doi:10.1364/ao.54.009823

Cited by 42 publications

(17 citation statements)

References 19 publications

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“…Therefore, we will compare the detection performance of DO2MR with some state-of-the-art methods in the automatic surface inspection field (some of them are designed not for the inspection of optical fiber end faces, but their inspection performance is excellent). They are Otsu’s method [ 20 ], the sigma-based segmentation (SBS) method [ 21 ], the improved-Otsu’s method [ 22 ], and the adaptive segmentation method (ASM) [ 23 ]. Otsu’s method is a commonly used threshold segmentation strategy.…”

Section: Experiments and Discussionmentioning

confidence: 99%

Automated Inspection of Defects in Optical Fiber Connector End Face Using Novel Morphology Approaches

Mei

Wang

Wen

et al. 2018

Sensors

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Increasing deployment of optical fiber networks and the need for reliable high bandwidth make the task of inspecting optical fiber connector end faces a crucial process that must not be neglected. Traditional end face inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. More seriously, the inspection results cannot be quantified for subsequent analysis. Aiming at the characteristics of typical defects in the inspection process for optical fiber end faces, we propose a novel method, “difference of min-max ranking filtering” (DO2MR), for detection of region-based defects, e.g., dirt, oil, contamination, pits, and chips, and a special model, a “linear enhancement inspector” (LEI), for the detection of scratches. The DO2MR is a morphology method that intends to determine whether a pixel belongs to a defective region by comparing the difference of gray values of pixels in the neighborhood around the pixel. The LEI is also a morphology method that is designed to search for scratches at different orientations with a special linear detector. These two approaches can be easily integrated into optical inspection equipment for automatic quality verification. As far as we know, this is the first time that complete defect detection methods for optical fiber end faces are available in the literature. Experimental results demonstrate that the proposed DO2MR and LEI models yield good comprehensive performance with high precision and accepted recall rates, and the image-level detection accuracies reach 96.0 and 89.3%, respectively.

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Section: Experiments and Discussionmentioning

confidence: 99%

Automated Inspection of Defects in Optical Fiber Connector End Face Using Novel Morphology Approaches

Mei

Wang

Wen

et al. 2018

Sensors

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“…This type of defect detection has been applied to glass plate [34,35], sheet metal [36] and aluminum strips [37]. A few, defect detection algorithms, in uniform surface images, use simple edge detection techniques [38]. They also use first-order statistics [39,40] such as mean and variance from the histogram of the defect image.…”

Section: Literature Surveymentioning

confidence: 99%

A simple computational framework for defect detection system with orthogonal polynomials transcoded coefficients

Krishnamoorthy

Ganesh

2016

Journal of Visual Communication and Image Representation

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“…Various image processing techniques, such as the threshold method, Otsu's method and Fourier transform [3][4][5] , have been implemented for defect detection to replace the timeconsuming and error-prone manual inspection. Scientists at the Lawrence Livermore National Laboratory (LLNL) have conducted a lot of valuable researches in the field of damage online inspection.…”

Section: Introductionmentioning

confidence: 99%

Detection of laser-induced optical defects based on image segmentation

Chu

Zhang

Tian

et al. 2019

High Pow Laser Sci Eng

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A number of vision-based methods for detecting laser-induced defects on optical components have been implemented to replace the time-consuming manual inspection. While deep-learning-based methods have achieved state-of-the-art performances in many visual recognition tasks, their success often hinges on the availability of a large number of labeled training sets. In this paper, we propose a surface defect detection method based on image segmentation with a U-shaped convolutional network (U-Net). The designed network was trained on paired sets of online and offline images of optics from a large laser facility. We show in our experimental evaluation that our approach can accurately locate laser-induced defects on the optics in real time. The main advantage of the proposed method is that the network can be trained end to end on small samples, without the requirement for manual labeling or manual feature extraction. The approach can be applied to the daily inspection and maintenance of optical components in large laser facilities.

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Surface defect detection method for glass substrate using improved Otsu segmentation

Cited by 42 publications

References 19 publications

Automated Inspection of Defects in Optical Fiber Connector End Face Using Novel Morphology Approaches

Automated Inspection of Defects in Optical Fiber Connector End Face Using Novel Morphology Approaches

A simple computational framework for defect detection system with orthogonal polynomials transcoded coefficients

Detection of laser-induced optical defects based on image segmentation

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