Weak scratch detection and defect classification methods for a large-aperture optical element

Tao, Xu; Xu, De; Zhang, Zhengtao; Zhang, Feng; Liu, Xi-Long; Zhang, David

doi:10.1016/j.optcom.2016.10.062

Cited by 38 publications

(17 citation statements)

References 16 publications

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“…Local binary patterns (LBP) [21] and a histogram of oriented gradient (HOG) [22] are the most used features. There are lots of other features, such as co-occurrence matrix (GLCM) [23] and some grayscale statistical features [24,25]. However, the above detection methods cannot be directly deployed to the metallic surface, since traditional image processing techniques are very sensitive to illumination and background clutter.…”

Section: Traditional Methodsmentioning

confidence: 99%

Deep Metallic Surface Defect Detection: The New Benchmark and Detection Network

Duan

Jiajia

et al. 2020

Sensors

218

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Metallic surface defect detection is an essential and necessary process to control the qualities of industrial products. However, due to the limited data scale and defect categories, existing defect datasets are generally unavailable for the deployment of the detection model. To address this problem, we contribute a new dataset called GC10-DET for large-scale metallic surface defect detection. The GC10-DET dataset has great challenges on defect categories, image number, and data scale. Besides, traditional detection approaches are poor in both efficiency and accuracy for the complex real-world environment. Thus, we also propose a novel end-to-end defect detection network (EDDN) based on the Single Shot MultiBox Detector. The EDDN model can deal with defects with different scales. Furthermore, a hard negative mining method is designed to alleviate the problem of data imbalance, while some data augmentation methods are adopted to enrich the training data for the expensive data collection problem. Finally, the extensive experiments on two datasets demonstrate that the proposed method is robust and can meet accuracy requirements for metallic defect detection.

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Section: Traditional Methodsmentioning

confidence: 99%

Deep Metallic Surface Defect Detection: The New Benchmark and Detection Network

Duan

Jiajia

et al. 2020

Sensors

218

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“…As a result, the camera responds weakly against these weak defects, as shown by the little bulge in the red circle in Figure 13. This system involves a threshold-segmentation method extracting suspected targets from original sub-apertures [25]. The threshold should be carefully chosen to achieve a good balance between detecting performance and false-alarm rate.…”

Section: Potential Enhancement Of Weak Defect Detection Through Targementioning

confidence: 99%

A New Stitching Method for Dark-Field Surface Defects Inspection Based on Simplified Target-Tracking and Path Correction

Xue

Sui

2020

Sensors

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A camera-based dark-field imaging system can effectively detect defects of microns on large optics by scanning and stitching sub-apertures with a small field of view. However, conventional stitching methods encounter problems of mismatches and location deviations, since few defects exist on the tested fine surface. In this paper, a highly efficient stitching method is proposed, based on a simplified target-tracking and adaptive scanning path correction. By increasing the number of sub-apertures and switching to camera perspective, the defects can be regarded as moving targets. A target-tracking procedure is firstly performed to obtain the marked targets. Then, the scanning path is corrected by minimizing the sum of deviations. The final stitching results are updated by re-using the target-tracking method. An experiment was carried out on an inspection of our specially designed testing sample. Subsequently, 118 defects were identified out of 120 truly existing defects, without stitching mismatches. The experiment results show that this method can help to reduce mismatches and location deviations of defects, and it was also effective in increasing the detectability for weak defects.

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“…In recent years, machine vision-based methods have gradually become a trend in the surface defect detection, because they can overcome many of the shortcomings of manual detection, including low accuracy, poor real-time performance, subjectivity, and high labor intensity. These machine vision-based inspection systems occur in many industrial applications, such as steel strip inspection [2,3], liquid crystal display (LCD) inspection [4], fabric inspection [5,6], aluminum profiles [7], railway track inspection [8], food inspection [9], and optical components inspection [10].…”

Section: Introductionmentioning

confidence: 99%

“…By analyzing the characteristics of the input image, the feature vector describing the defect information is designed, and then the feature vector is put into a classifier model that is trained in advance to determine whether the input image has a defect or not. These features include the local binary patterns (LBP) feature [2], a gray level co-occurrence matrix (GLCM) [7], a histogram of oriented gradient (HOG) features [25], and other grayscale statistical features [8,10]. Although those detection algorithms have achieved better detection results in various surface defect detection, these cannot be directly applied to the aforementioned metallic surface.…”

Section: Introductionmentioning

confidence: 99%

Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks

et al. 2018

Self Cite

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Automatic metallic surface defect inspection has received increased attention in relation to the quality control of industrial products. Metallic defect detection is usually performed against complex industrial scenarios, presenting an interesting but challenging problem. Traditional methods are based on image processing or shallow machine learning techniques, but these can only detect defects under specific detection conditions, such as obvious defect contours with strong contrast and low noise, at certain scales, or under specific illumination conditions. This paper discusses the automatic detection of metallic defects with a twofold procedure that accurately localizes and classifies defects appearing in input images captured from real industrial environments. A novel cascaded autoencoder (CASAE) architecture is designed for segmenting and localizing defects. The cascading network transforms the input defect image into a pixel-wise prediction mask based on semantic segmentation. The defect regions of segmented results are classified into their specific classes via a compact convolutional neural network (CNN). Metallic defects under various conditions can be successfully detected using an industrial dataset. The experimental results demonstrate that this method meets the robustness and accuracy requirements for metallic defect detection. Meanwhile, it can also be extended to other detection applications.

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Weak scratch detection and defect classification methods for a large-aperture optical element

Cited by 38 publications

References 16 publications

Deep Metallic Surface Defect Detection: The New Benchmark and Detection Network

Deep Metallic Surface Defect Detection: The New Benchmark and Detection Network

A New Stitching Method for Dark-Field Surface Defects Inspection Based on Simplified Target-Tracking and Path Correction

Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks

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