Artificial Intelligence Based Multi-object Inspection System

2019 ◽  
pp. 205-211 ◽  
Author(s):  
Santosh Kumar Sahoo
Keyword(s):  
Artificial Intelligence ◽  
Inspection System ◽  
Object Inspection

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.

An Artificial Intelligent Centered Object Inspection System Using Crucial Images

2018 ◽  
Vol 5 (1) ◽  
pp. 44-57 ◽  
Author(s):  
Santosh Kumar Sahoo ◽  
B. B. Choudhury
Keyword(s):  
Principal Component ◽  
Cuckoo Search ◽  
Basic Function ◽  
Least Square ◽  
Support Vector ◽  
Inspection System ◽  
Bench Mark ◽  
Test Functions ◽  
Proposed Model ◽  
Object Inspection

This article proposes a unique optimization algorithm like Adaptive Cuckoo Search (AdCS) algorithm followed by an Intrinsic Discriminant Analysis (IDA) to design an intelligent object classifier for inspection of defective object like bottle in a manufacturing unit. By using this methodology the response time is very faster than the other techniques. The projected scheme is authenticated using different bench mark test functions along with an effective inspection procedure for identification of bottle by using AdCS, Principal-Component-Analysis (PCA) and IDA. Due to this the projected procedures terms as PCA+IDA for dimension reduction in addition to this AdCS-IDA for classification or identification of defective bottles. The analyzed response obtained from by an application of AdCS algorithm followed by IDA and compared to other algorithm like Least-Square-Support-Vector-Machine (LSSVM), Linear Kernel Radial-Basic-Function (RBF) to the proposed model, the earlier applied scheme reveals the remarkable performance.

Low-Power Drone-Mountable Real-Time Artificial Intelligence Framework for Road Asset Classification

2020 ◽  
pp. 036119812096517
Author(s):  
Shrey Mohan ◽  
Omidreza Shoghli ◽  
Adrian Burde ◽  
Hamed Tabkhi
Keyword(s):  
Artificial Intelligence ◽  
Low Power ◽  
Real Time ◽  
Total Power ◽  
Inspection System ◽  
Highway Traffic ◽  
Labor Costs ◽  
Continuous Increase ◽  
Embedded Platforms ◽  
Road Inspection

With the continuous increase in interstate highway traffic and demand for higher safety standards, there is a growing need for rapidly scalable road inspection. Currently, inspection and condition assessment of roadways involve manual operations which increase labor costs and limit the scalability and inspection coverage. Furthermore, manually inspecting highways adds additional safety risks for highway workers and road inspectors. To address these challenges, we envision a fully automated process of highway inspection. This paper presents a novel low-power drone-mountable real-time artificial intelligence (AI) framework for road asset classification through visual sensing, which is the first step toward a fully automated inspection system. We analyzed a state DOT dataset, consisting of 14 different kinds of defected road assets. To this end, we developed our baseline framework using MobileNet-V2, which is a convolutional neural network (CNN) specially developed for mobile and embedded platforms. Since our target dataset was small and CNNs networks require a huge amount of data, we leveraged transfer learning, by pretraining MobileNet-V2 using the ImageNet dataset and then fine-tuned it on our target dataset. This new framework was ported to embedded platforms Nvidia Jetson Nano with the capability to perform on-board drone processing. Overall, our results demonstrate 81.33% accuracy on the test set while processing 7.4 frames per second and occupying a total power of 1.9 W. It achieved a Power Reduction Factor (PRF) of 21.17 over Nvidia TitanV implementation, with only 8.74% impact on the projected drone flight time.

scholarly journals AUTOMATED INSPECTION OF FRUITS AND VEGETABLES

HortScience ◽  
1991 ◽  
Vol 26 (6) ◽  
pp. 712B-712
Author(s):  
C. Morrow ◽  
P Heinemann ◽  
H. Sommer ◽  
R. Crassweller ◽  
R. Cole ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Fruits And Vegetables ◽  
Surface Defects ◽  
Quality Score ◽  
Inspection System ◽  
Automated Inspection ◽  
Monochromatic Image ◽  
Manual Inspection ◽  
Current Emphasis

Research is described on the development of an automated inspection system which uses digital images and artificial intelligence techniques. Procedures have been developed for evaluating size, shape, and color of apples, potatoes, and mushrooms. Current emphasis is being placed on developing algorithms for detection of surface defects. A major effort will also be expended toward the development of an overall “quality” score for automated inspection of fruit and vegetables. The automated results are compared with those obtained using conventional manual inspection methods. Apples, potatoes, and mushrooms are the primary crops being inspected although the algorithms and techniques are applicable to many different fruits and vegetables. Color and monochromatic image processing components in “MS-DOS” and “Macintosh” computers are being used in this study.

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