Automatic inspection system of surface defects on optical IR-CUT filter based on machine vision

Liu, Yan; Yu, Feihong

doi:10.1016/j.optlaseng.2013.11.013

Cited by 47 publications

(18 citation statements)

References 36 publications

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“…This section introduces our image acquisition system, including light source setting, camera parameters setting, image capturing design, and so on. To normalize illumination variation and increase the contrast between defect and background, lighting systems have been considered in this work, such as background lighting [11], parallel lighting [12], or infrared lighting [13]. Before choosing a suitable light source, it is necessary to prioritize the influence of the light source.…”

Section: Image Acquisition Systemmentioning

confidence: 99%

Novel Framework Based on HOSVD for Ski Goggles Defect Detection and Classification

Wang

et al. 2019

Sensors

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No matter your experience level or budget, there is a great ski goggle waiting to be found.Goggles are an essential part of skiing or snowboarding gear to protect your eyes from harsh environmental elements and injury. In the ski goggles manufacturing industry, defects, especially on the lens surface, are unavoidable. However, defect detection and classification by visual inspection in the manufacturing process is very difficult. To overcome this problem, a novel framework based on machine vision is presented, named as the ski goggles lens defect detection, with five high-resolution cameras and custom-made lighting field to achieve a high-quality ski goggles lens image. Next, the defects on the lens of ski goggles are detected by using parallel projection in opposite directions based on adaptive energy analysis. Before being put into the classification system, the defect images are enhanced by an adaptive method based on the high-order singular value decomposition (HOSVD). Finally, dust and five types of defect images are classified into six types, i.e., dust, spotlight (type 1, type 2, type 3), string, and watermark, by using the developed classification algorithm. The defect detection and classification results of the ski goggles lens are compared to the standard quality of the manufacturer. Experiments using 120 ski goggles lens samples collected from the largest manufacturer in Taiwan are conducted to validate the performance of the proposed framework. The accurate defect detection rate is 100% and the classification accuracy rate is 99.3%, while the total running time is short. The results demonstrate that the proposed method is sound and useful for ski goggles lens inspection in industries.

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Section: Image Acquisition Systemmentioning

confidence: 99%

Novel Framework Based on HOSVD for Ski Goggles Defect Detection and Classification

Wang

et al. 2019

Sensors

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“…The decomposition procedures of SWT are shown in Fig.1, in addition, the relationship between filter L j and H j can be also visualized in it [22]. …”

Section: Proposed Fusion Schemementioning

confidence: 99%

A fast and automatic fusion algorithm for unregistered multi-exposure image sequence

Liu

2014

SPIE Proceedings

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Human visual system (HVS) can visualize all the brightness levels of the scene through visual adaptation. However, the dynamic range of most commercial digital cameras and display devices are smaller than the dynamic range of human eye. This implies low dynamic range (LDR) images captured by normal digital camera may lose image details. We propose an efficient approach to high dynamic (HDR) image fusion that copes with image displacement and image blur degradation in a computationally efficient manner, which is suitable for implementation on mobile devices. The various image registration algorithms proposed in the previous literatures are unable to meet the efficiency and performance requirements in the application of mobile devices. In this paper, we selected Oriented Brief (ORB) detector to extract local image structures. The descriptor selected in multi-exposure image fusion algorithm has to be fast and robust to illumination variations and geometric deformations. ORB descriptor is the best candidate in our algorithm. Further, we perform an improved RANdom Sample Consensus (RANSAC) algorithm to reject incorrect matches. For the fusion of images, a new approach based on Stationary Wavelet Transform (SWT) is used. The experimental results demonstrate that the proposed algorithm generates high quality images at low computational cost. Comparisons with a number of other feature matching methods show that our method gets better performance.

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“…With the advancement of computer vision or intelligent inspection, many surface defect detection methods are reported in the literature [5][6][7][8] . Particularly, in literature [5], weak scratches and cracks in high-curvature optical lenses were automatically detected using a coarse-to-fine detection strategy applied to complicated dark-field images.…”

Section: Introductionmentioning

confidence: 99%

Defect Detection and Full Surface Characterization of High Curvature Cathode Filaments

Yi¹,

Huang²,

Xie³

et al. 2019

Proceedings of the 2019 International Conference on Modeling, Analysis, Simulation Technologies and Applications (MASTA 2019)

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Surface defects of cathode filaments of microwave magnetron would cause magnetron failure and scrapped microwave systems. Therefore, surface defects on cathode filaments must be carefully inspected. Conventionally, filaments are manually and visually inspected using their amplified images under an optical microscope. This is because automatic defect detection of cathode filaments is a challenging problem. The difficulty comings from its complex surface shape with multiple turns of high curvature spiral circles, which occlude each other. Such complex shape prevents capturing of sharp focusing images, which are essential for a computerized automatic detection algorithm. Further, the variable nature of production defects complicated the process of automatic defect detection task. To solve these problems, this paper proposes an automatic defect detection method to deal with issues related to complex shapes containing occlusions as well as high curvatures, particularly for the quality inspection of spiral shaped cathode filaments. The method includes a novel digital scanner, which sequentially brings all sections of the filament sides into sharp focusing of the optical imaging system. The method also employs multiple optical systems to imaging multi-sides of the spiral filament. The computational algorithm primarily uses line-detectors. In an evaluation experiment, the proposed method was used to automatically inspect over 14 million cathode filaments. Experimental results indicate that its false negative rate was 0.0065%, and its false positive rate was 6.83%. This indicates that the proposed method could successfully detect all kinds of surface defects at over 99.99% accuracy. It reduces the workload for manual inspection from 100% down to 93.17%, over an order of magnitude reduction. Further, the efficiency of the proposed method is 70 spiral filaments per minute, satisfying the requirements of online quality detection of existing manufacturing lines of filament cathodes.

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Automatic inspection system of surface defects on optical IR-CUT filter based on machine vision

Cited by 47 publications

References 36 publications

Novel Framework Based on HOSVD for Ski Goggles Defect Detection and Classification

Novel Framework Based on HOSVD for Ski Goggles Defect Detection and Classification

A fast and automatic fusion algorithm for unregistered multi-exposure image sequence

Defect Detection and Full Surface Characterization of High Curvature Cathode Filaments

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