An efficient similarity measure approach for PCB surface defect detection

Gaidhane, Vilas H.; Hote, Yogesh V.; Singh, Vijander

doi:10.1007/s10044-017-0640-9

Cited by 75 publications

(41 citation statements)

References 26 publications

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“…However, this method requires a noise-free environment and perfect alignment of the sample images [117]. Gaidhane et al in [199] also proposed a similarity approach to detect PCB surface defects. As in the previous study, a reference template image that represents the ideal PCB state is generated to assess its similarity with the sample inspected images.…”

Section: D: Hough Transformmentioning

confidence: 99%

“…However, this solution is considered costly and needs special alignment. 3D optical [176] Adaptive template Rule-based 100% 0% -ViBe, SDL, SAM& RPCA [199] Systematic matrix Rule-based Up to 100% 0.05% 1.64s (total) [52], [198], [407], [408] [196] Textural Adaptive thresholding Rule-based 78% (precision) ->0.203s/image - [246] ICA Rule-based --8.1ms/image - [249] Optical flow Rule-based --0.054s/image - [248] Modified Hough transform Rule-based -0% 0.696s/image Hough transform [271] Segmentation F-SVDD 95% -7.8s/panel SVDD [271] Segmentation QK-SVDD 96% 7.54% 60ms/panel SVDD [267] PCA [415] can be considered an effective tool to mitigate these problems and to provide full description of the component 3D nature.…”

Section: Limitations Of Aoi Systems and Future Directionsmentioning

confidence: 99%

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A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

2020

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Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, microelectronics , and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.

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Section: D: Hough Transformmentioning

confidence: 99%

Section: Limitations Of Aoi Systems and Future Directionsmentioning

confidence: 99%

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

2020

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“…They used a trend peak algorithm to extract the hub defect area and then a BP neural network to classify and identify the hub defect. In order to complete surface defect detection of printed circuit boards (PCBs), an effective similarity measurement method has been proposed [14]. This method uses the adjoint matrix of two comparative images to calculate the symmetric matrix.…”

Section: Related Workmentioning

confidence: 99%

Surface Defects Recognition of Wheel Hub Based on Improved Faster R-CNN

Sun

Huang

et al. 2019

Electronics

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Machine vision is one of the key technologies used to perform intelligent manufacturing. In order to improve the recognition rate of multi-class defects in wheel hubs, an improved Faster R-CNN method was proposed. A data set for wheel hub defects was built. This data set consisted of four types of defects in 2,412 1080 × 1440 pixels images. Faster R-CNN was modified, trained, verified and tested based on this database. The recognition rate for this proposed method was excellent. The proposed method was compared with the popular R-CNN and YOLOv3 methods showing simpler, faster, and more accurate defect detection, which demonstrates the superiority of the improved Faster R-CNN for wheel hub defects.

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“…Despite these numerous studies, the detection of surface defects has seldom been reported. In the field of automatic detection of surface 2 of 15 defects, some scholars have studied detection methods for the characteristics of steel, glass, and other materials [10][11][12][13][14][15][16][17][18][19], but few have studied the surface defect detection technology on particleboards. In Reference [10], classical convolutional neural networks (CNNs) trained in pure supervised manner was used to detect defects on steel surfaces.…”

Section: Introductionmentioning

confidence: 99%

“…The combination of subtraction and projection (CSP) was used to identify defects on the MPSG image, which could eliminate the influence of fluctuation in ambient illumination. Reference [17] proposed an efficient similarity measure for the detection of surface defects in printed circuit boards (PCB). The method could measure the similarity between the scene image and the reference image of PCB surface without the need to compute image features such as eigenvalues and eigenvectors.…”

Section: Introductionmentioning

confidence: 99%

Extraction and Detection of Surface Defects in Particleboards by Tracking Moving Targets

2018

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Considering the linear motion of particleboards in the production line, the detection of surface defects in particleboards is a major challenge. In this paper, a method based on moving target tracking is proposed for the detection of surface defects in particleboards. To achieve this, the kernel correlation filter (KCF) target tracking algorithm was modified with the median flow algorithm and used to capture the moving targets of surface defects. The defect images were extracted by a Sobel operator, and the defect number, the defect area, and the degree of damage were calculated. The level of surface defect in particleboards was evaluated by fuzzy pattern recognition. Experiments were then carried out to prove the effectiveness and accuracy of the proposed method.

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An efficient similarity measure approach for PCB surface defect detection

Cited by 75 publications

References 26 publications

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

Surface Defects Recognition of Wheel Hub Based on Improved Faster R-CNN

Extraction and Detection of Surface Defects in Particleboards by Tracking Moving Targets

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