Visual inspection system for the classification of solder joints

1999 ◽  
Vol 32 (4) ◽  
pp. 565-575 ◽  
Author(s):  
Tae-Hyeon Kim ◽  
Tai-Hoon Cho ◽  
Young Shik Moon ◽  
Sung Han Park
Keyword(s):  
Visual Inspection ◽  
Solder Joints ◽  
Inspection System

Detection of Insects inside Wheat Kernels by NIR Imaging

1998 ◽  
Vol 6 (1) ◽  
pp. 115-119 ◽  
Author(s):  
Christopher Ridgway ◽  
John Chambers
Keyword(s):  
Visual Inspection ◽  
Spectroscopic Studies ◽  
Inspection System ◽  
Naked Eye ◽  
Nir Imaging ◽  
Reliable Classification ◽  
And Control ◽  
Wheat Kernels ◽  
Poor Lighting

The research reported here forms the basis to extend the capabilities of a newly-developed grain contaminant inspection system to include detection of kernels infested internally with insects. It was found that wheat kernels infested internally with larvae of Sitophilus granarius (grain weevil) had markedly different appearances from uninfested kernels (controls) when imaged at certain wavelengths in the NIR. Imaging at the single wavelength 1202 nm highlighted consistent differences between all 10 infested kernels and all 10 controls in the sample. Infested kernels exhibited light patches which covered a large proportion of the surface. Uninfested kernels appeared uniformly dark. At this wavelength, possible interference from dark mould on the germ of the kernel (black point) was also removed. Imaging at two wavelengths with subtraction (1202 – 1300 nm) appeared to give further enhancement of differences between infested and control kernels. However, one infested kernel remained indistinguishable from the controls which may have been due to poor lighting or signal-to-noise. The findings are consistent with previous spectroscopic studies which indicated that similar wavelengths had potential to resolve the insect from the kernel. Although the infested kernels were seen by the naked eye to be slightly different to the control kernels, these visible differences were not obvious or consistent. It is unlikely that reliable classification of kernels by visual inspection would prove possible. This study suggests that imaging in the NIR region improves differences in appearance to a point where reliable and rapid classification is possible. The next step will be to test this approach on unknown samples and obtain accuracy in classification.

Metal Parts Visual Inspection Based on Production Rules

2011 ◽  
Vol 110-116 ◽  
pp. 4091-4095
Author(s):  
Sh. Hashim Haider ◽  
Anton Satria Prabuwono ◽  
Norul Huda Sheikh Abdullah Siti
Keyword(s):  
Visual Inspection ◽  
Manufacturing Industry ◽  
Inspection System ◽  
Automated Visual Inspection ◽  
Production Rules ◽  
Metal Part ◽  
Mechanical Parts ◽  
The Past ◽  
Web Camera

In manufacturing industry the automated visual inspection system (AVIS) is a method to inspect, classify and detect defects of various products. In the past, the tasks of inspection are carrying out by humans, machines or both. In this paper, we account for an AVIS model to classify mechanical parts in production line. It comprises two parts: hardware and software. The model uses a web-camera attached to an adjustable stand to capture various group of metal part images. The main objective is to develop an intelligent inspection tool based on image processing and production rules. It computes both the area and circularity of mechanical shapes as the features and hence classifies them according to ten categories such as screws, nuts, and bolts at different sizes. The result shows that the accuracy is 91.5% for group and 98.25% for individual classification of mechanical parts subsequently.

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.

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