Artificial intelligence-based automatic visual inspection system for built heritage

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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Robotic inspection of ship hull surfaces using a magnetic crawler and a monocular camera

Sensor Review ◽

10.1108/sr-02-2017-0021 ◽

2017 ◽

Vol 37 (4) ◽

pp. 425-435 ◽

Cited By ~ 5

Author(s):

Annalisa Milella ◽

Rosalia Maglietta ◽

Massimo Caccia ◽

Gabriele Bruzzone

Keyword(s):

Visual Inspection ◽

Low Cost ◽

New Technology ◽

Experimental Tests ◽

Ship Hull ◽

Inspection System ◽

Content Type ◽

Monocular Camera ◽

Mosaic Image ◽

Automatic Visual Inspection

Purpose Periodic inspection of large tonnage vessels is critical to assess integrity and prevent structural failures that could have catastrophic consequences for people and the environment. Currently, inspection operations are undertaken by human surveyors, often in extreme conditions. This paper aims to present an innovative system for the automatic visual inspection of ship hull surfaces, using a magnetic autonomous robotic crawler (MARC) equipped with a low-cost monocular camera. Design/methodology/approach MARC is provided with magnetic tracks that make it able to climb along the vertical walls of a vessel while acquiring close-up images of the traversed surfaces. A homography-based structure-from-motion algorithm is developed to build a mosaic image and also produce a metric representation of the inspected areas. To overcome low resolution and perspective distortion problems in far field due to the tilted and low camera position, a “near to far” strategy is implemented, which incrementally generates an overhead view of the surface, as long as it is traversed by the robot. Findings This paper demonstrates the use of an innovative robotic inspection system for automatic visual inspection of vessels. It presents and validates through experimental tests a mosaicking strategy to build a global view of the structure under inspection. The use of the mosaic image as input to an automatic corrosion detector is also demonstrated. Practical implications This paper may help to automate the inspection process, making it feasible to collect images from places otherwise difficult or impossible to reach for humans and automatically detect defects, such as corroded areas. Originality/value This paper provides a useful step towards the development of a new technology for automatic visual inspection of large tonnage ships.

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User password repetitive patterns analysis and visualization

Information and Computer Security ◽

10.1108/ics-06-2015-0026 ◽

2016 ◽

Vol 24 (1) ◽

pp. 93-115 ◽

Cited By ~ 2

Author(s):

Xiaoying Yu ◽

Qi Liao

Keyword(s):

Security Policy ◽

Large Data ◽

Efficient Algorithms ◽

Privacy And Security ◽

Data Set ◽

Web Based ◽

Content Type ◽

Word Cloud ◽

Individual Privacy ◽

Password Security

Purpose – Passwords have been designed to protect individual privacy and security and widely used in almost every area of our life. The strength of passwords is therefore critical to the security of our systems. However, due to the explosion of user accounts and increasing complexity of password rules, users are struggling to find ways to make up sufficiently secure yet easy-to-remember passwords. This paper aims to investigate whether there are repetitive patterns when users choose passwords and how such behaviors may affect us to rethink password security policy. Design/methodology/approach – The authors develop a model to formalize the password repetitive problem and design efficient algorithms to analyze the repeat patterns. To help security practitioners to analyze patterns, the authors design and implement a lightweight, Web-based visualization tool for interactive exploration of password data. Findings – Through case studies on a real-world leaked password data set, the authors demonstrate how the tool can be used to identify various interesting patterns, e.g. shorter substrings of the same type used to make up longer strings, which are then repeated to make up the final passwords, suggesting that the length requirement of password policy does not necessarily increase security. Originality/value – The contributions of this study are two-fold. First, the authors formalize the problem of password repetitive patterns by considering both short and long substrings and in both directions, which have not yet been considered in past. Efficient algorithms are developed and implemented that can analyze various repeat patterns quickly even in large data set. Second, the authors design and implement four novel visualization views that are particularly useful for exploration of password repeat patterns, i.e. the character frequency charts view, the short repeat heatmap view, the long repeat parallel coordinates view and the repeat word cloud view.

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Automatic visual inspection system for solder paste deposition in PCBs

2021 Telecoms Conference (ConfTELE) ◽

10.1109/conftele50222.2021.9435522 ◽

2021 ◽

Author(s):

Guilherme B. F. Paulo ◽

Luis M. R. Perdigoto ◽

Sergio M. M. Faria

Keyword(s):

Visual Inspection ◽

Inspection System ◽

Solder Paste ◽

Automatic Visual Inspection

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Functional neuronavigation with magnetoencephalography: outcome in 50 patients with lesions around the motor cortex

Journal of Neurosurgery ◽

10.3171/jns.1999.91.1.0073 ◽

1999 ◽

Vol 91 (1) ◽

pp. 73-79 ◽

Cited By ~ 160

Author(s):

Oliver Ganslandt ◽

Rudolf Fahlbusch ◽

Christopher Nimsky ◽

Helmut Kober ◽

Martin Möller ◽

...

Keyword(s):

Motor Cortex ◽

Three Dimensional ◽

Image Data ◽

Data Set ◽

Content Type ◽

Promising Tool ◽

Neuronavigation System ◽

Functional Neuronavigation ◽

Intraoperative Recording ◽

Fine Print

Object. The authors conducted a study to evaluate the clinical outcome in 50 patients with lesions around the motor cortex who underwent surgery in which functional neuronavigation was performed.Methods. The sensorimotor cortex was identified in all patients with the use of magnetoencephalography (MEG). The MEG-source localizations were superimposed onto a three-dimensional magnetic resonance image and the image data set was implemented into a neuronavigation system. Based on this setup, the surgeon chose the best surgical strategy. During surgery, the pre- and postcentral gyri were identified by neuronavigation and, in addition, the central sulcus was localized using intraoperative recording of somatosensory evoked potentials. In all cases MEG localizations of the sensory or motor cortex were correct. In 30% of the patients preoperative paresis improved, in 66% no additional deficits occurred, and in only 4% (two patients) deterioration of neurological function occurred. In one of these patients the deterioration was not related to the procedure.Conclusions. The method of incorporating functional data into neuronavigation systems is a promising tool that can be used in more radical surgery to lessen morbidity around eloquent brain areas.

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