On-Line Automatic Visual Inspection Of Internal Surfaces

1983 ◽  
Author(s):  
M. J. Closier ◽  
S. C. Sood
Keyword(s):  
Visual Inspection ◽  
Internal Surfaces ◽  
Automatic Visual Inspection ◽  
On Line

Automatic Visual Inspection Machine for Pharmaceutical Infusion Bags Implementing Cellular Neural Networks

2021 ◽  
Author(s):  
Francesco Marrone ◽  
Gianluca Zoppo ◽  
Luca Vescovi ◽  
Filippo Begarani ◽  
Ada Palama ◽  
...  
Keyword(s):  
Neural Networks ◽  
Visual Inspection ◽  
Cellular Neural Networks ◽  
Automatic Visual Inspection ◽  
Inspection Machine

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.

Impact of Feedback Training in Cbt in Visual Inspection

2002 ◽  
Vol 46 (25) ◽  
pp. 2079-2083 ◽  
Author(s):  
Jiao Ma ◽  
Colin G. Drury ◽  
Ann M. Bisantz
Keyword(s):  
Performance Measures ◽  
Performance Feedback ◽  
Visual Inspection ◽  
Search Time ◽  
Difficulty Level ◽  
Process Assessment ◽  
Computer Based Training ◽  
Feedback Training ◽  
On Line ◽  
Inspection Performance

Training has been a consistently effective intervention in improving inspection performance. For example, existing inspection training in the aircraft maintenance domain is mainly a combination of classroom and on-the-job training (OJT). Computer-based training (CBT) has been promoted ever since it was introduced to this domain. In this study we investigate how effectively feedback training can be combined with CBT to improve visual inspection performance. Specifically, we examine the potential positive impacts of performance and process feedback in CBT, given in an on-line manner, on a trainee's performance and process assessment in a visual inspection task. The CBT system for inspection we used was adopted from the ASSIST program (Chen, Gramopadhye and Melloy, 2000). In our computer simulation of a familiar situation, participants were asked to search certain areas inside of a car in order to detect certain targets (dropped coins) with the aid of computerized tools (e.g., a magnifying glass, a flashlight), and fill out an inspection report based upon detection. A significant test effect was found across performance measures. Type of feedback training was found to be significant for search time. Performance measures were significantly correlated with target difficulty level; on-line performance feedback was significantly more efficient in improving performance measures than conventional delayed performance feedback; feedback training did affect process assessment measures.

On-line Visual Inspection Of Automobile Display Panels

1985 ◽  
Author(s):  
R M. Atkinson ◽  
J F. Claridge ◽  
S R. Hattersley ◽  
J C. Taunton
Keyword(s):  
Visual Inspection ◽  
On Line
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