scholarly journals Laser Diffuse Lighting in a Visual Inspection System for Defect Detection in Wood Laminates

10.5772/21652 ◽  
2011 ◽  
Author(s):  
David Martin ◽  
Maria C. ◽  
Domingo Guine
Keyword(s):  
Defect Detection ◽  
Visual Inspection ◽  
Inspection System

A Soldering Defect Inspection System of a Special Integrated Circuit Board Based on Computer Vision

2013 ◽  
Vol 650 ◽  
pp. 543-547
Author(s):  
Cong Ling Zhou ◽  
Jun Qiang Wu ◽  
Yong Qiang Wang ◽  
Zeng Pu Xu
Keyword(s):  
Computer Vision ◽  
Defect Detection ◽  
Integrated Circuit ◽  
Visual Inspection ◽  
Ccd Camera ◽  
Structure Design ◽  
Circuit Board ◽  
Inspection System ◽  
Defect Inspection ◽  
Hardware System

This paper introduces a soldering defect inspection system for a special integrated circuit board aided by the computer vision. Space occluder is fixed on this special integrated circuit board, which makes the light blocked from the CCD camera to the chip pins to be inspected. This system can inspect the light blocked soldering defects of the chip pins through the structure design of hardware system and the software system. It is a cheap but automatic soldering defect inspecting system, and can do the soldering defect detection instead of manual visual inspection, and improve the detection speed and stability.

scholarly journals Automatic segmentation of ceramic materials with relaxed possibilistic C-Means clustering for defect detection

2020 ◽  
Vol 19 (3) ◽  
pp. 1505
Author(s):  
Kwang Baek Kim ◽  
Doo Heon Song ◽  
Hyun Jun Park
Keyword(s):  
Quality Control ◽  
Defect Detection ◽  
Visual Inspection ◽  
Clustering Algorithm ◽  
Automatic Segmentation ◽  
Ceramic Materials ◽  
Inspection System ◽  
Important Process ◽  
Better Than

<span>Auromatic inspection system is necessary for reliable quality control if ceramic materials to avoid operator subjectivity and fatigue in visual inspection. Automatic segmentation from material’s image is then the most important process to develop such an inspection system. In this paper, we propose a Possibilistic C-Means pixel clustering algorithm with fuzzy stretching to form the defect object in segmentation. In experiment using 50 images containing a certain amount of defects, the proposed method was successful in 49 cases or 98% of opportunities. That performance is roughly twice better than that of standard K-means clustering in defect object formation</span><span>Auromatic inspection system is necessary for reliable quality control if ceramic materials to avoid operator subjectivity and fatigue in visual inspection. Automatic segmentation from material’s image is then the most important process to develop such an inspection system. In this paper, we propose a Possibilistic C-Means pixel clustering algorithm with fuzzy stretching to form the defect object in segmentation. In experiment using 50 images containing a certain amount of defects, the proposed method was successful in 49 cases or 98% of opportunities. That performance is roughly twice better than that of standard K-means clustering in defect object formation.</span>

scholarly journals A novel visual inspection system for rail surface spalling detection

2021 ◽  
Vol 1048 (1) ◽  
pp. 012015
Author(s):  
Dieuthuy Pham ◽  
Minhtuan Ha ◽  
Changyan Xiao
Keyword(s):  
Visual Inspection ◽  
Inspection System

Visual inspection system using multidirectional 3-D imager

1991 ◽  
Author(s):  
Tetsuo Koezuka ◽  
Yoshikazu Kakinoki ◽  
Shinji Hashinami ◽  
Masato Nakashima
Keyword(s):  
Visual Inspection ◽  
Inspection System

Artificial intelligence-based automatic visual inspection system for built heritage

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lukman E. Mansuri ◽  
D.A. Patel
Keyword(s):  
Artificial Intelligence ◽  
Visual Inspection ◽  
Image Data ◽  
Inspection System ◽  
Data Set ◽  
Web Based ◽  
Content Type ◽  
Built Heritage ◽  
Automatic Visual Inspection ◽  
Inspection Systems

PurposeHeritage is the latent part of a sustainable built environment. Conservation and preservation of heritage is one of the United Nations' (UN) sustainable development goals. Many social and natural factors seriously threaten heritage structures by deteriorating and damaging the original. Therefore, regular visual inspection of heritage structures is necessary for their conservation and preservation. Conventional inspection practice relies on manual inspection, which takes more time and human resources. The inspection system seeks an innovative approach that should be cheaper, faster, safer and less prone to human error than manual inspection. Therefore, this study aims to develop an automatic system of visual inspection for the built heritage.Design/methodology/approachThe artificial intelligence-based automatic defect detection system is developed using the faster R-CNN (faster region-based convolutional neural network) model of object detection to build an automatic visual inspection system. From the English and Dutch cemeteries of Surat (India), images of heritage structures were captured by digital camera to prepare the image data set. This image data set was used for training, validation and testing to develop the automatic defect detection model. While validating this model, its optimum detection accuracy is recorded as 91.58% to detect three types of defects: “spalling,” “exposed bricks” and “cracks.”FindingsThis study develops the model of automatic web-based visual inspection systems for the heritage structures using the faster R-CNN. Then it demonstrates detection of defects of spalling, exposed bricks and cracks existing in the heritage structures. Comparison of conventional (manual) and developed automatic inspection systems reveals that the developed automatic system requires less time and staff. Therefore, the routine inspection can be faster, cheaper, safer and more accurate than the conventional inspection method.Practical implicationsThe study presented here can improve inspecting the built heritages by reducing inspection time and cost, eliminating chances of human errors and accidents and having accurate and consistent information. This study attempts to ensure the sustainability of the built heritage.Originality/valueFor ensuring the sustainability of built heritage, this study presents the artificial intelligence-based methodology for the development of an automatic visual inspection system. The automatic web-based visual inspection system for the built heritage has not been reported in previous studies so far.

Application of the SEMSpec electron-beam inspection system to in-process defect detection on semiconductor wafers

1996 ◽  
Vol 30 (1-4) ◽  
pp. 567-570 ◽  
Author(s):  
T.R. Cass ◽  
D. Hendricks ◽  
J. Jau ◽  
H.J. Dohse ◽  
A.D. Brodie ◽  
...  
Keyword(s):  
Electron Beam ◽  
Defect Detection ◽  
Inspection System

scholarly journals Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5136
Author(s):  
Xiaoxin Fang ◽  
Qiwu Luo ◽  
Bingxing Zhou ◽  
Congcong Li ◽  
Lu Tian
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Three Dimensional ◽  
Image Features ◽  
Stereoscopic Vision ◽  
Research Progress ◽  
Inspection System ◽  
Two Dimensional ◽  
Surface Defect Detection ◽  
Planar Materials

The computer-vision-based surface defect detection of metal planar materials is a research hotspot in the field of metallurgical industry. The high standard of planar surface quality in the metal manufacturing industry requires that the performance of an automated visual inspection system and its algorithms are constantly improved. This paper attempts to present a comprehensive survey on both two-dimensional and three-dimensional surface defect detection technologies based on reviewing over 160 publications for some typical metal planar material products of steel, aluminum, copper plates and strips. According to the algorithm properties as well as the image features, the existing two-dimensional methodologies are categorized into four groups: statistical, spectral, model, and machine learning-based methods. On the basis of three-dimensional data acquisition, the three-dimensional technologies are divided into stereoscopic vision, photometric stereo, laser scanner, and structured light measurement methods. These classical algorithms and emerging methods are introduced, analyzed, and compared in this review. Finally, the remaining challenges and future research trends of visual defect detection are discussed and forecasted at an abstract level.

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