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Previous research has shown that “Scan-vs-BIM” object recognition systems, which fuse three dimensional (3D) point clouds from terrestrial laser scanning (TLS) or digital photogrammetry with 4D project building information models (BIM), provide valuable information for tracking construction works. However, until now, the potential of these systems has been demonstrated for tracking progress of permanent structural works only; no work has been reported yet on tracking secondary or temporary structures. For structural concrete work, temporary structures include formwork, scaffolding and shoring, while secondary components include rebar. Together, they constitute most of the earned value in concrete work. The impact of tracking secondary and temporary objects would thus be added veracity and detail to earned value calculations, and subsequently better project control and performance. The paper aims to discuss these issues.

Two techniques for recognizing concrete construction secondary and temporary objects in TLS point clouds are implemented and tested using real-life data collected from a reinforced concrete building construction site. Both techniques represent significant innovative extensions of existing “Scan-vs-BIM” object recognition frameworks.

The experimental results show that it is feasible to recognise secondary and temporary objects in TLS point clouds with good accuracy using the two novel techniques; but it is envisaged that superior results could be achieved by using additional cues such as colour and 3D edge information.

This article makes valuable contributions to the problem of detecting and tracking secondary and temporary objects in 3D point clouds. The power of Scan-vs-BIM object recognition approaches to address this problem is demonstrated, but their limitations are also highlighted.

The field of machine vision, or computer vision, has been growing at fast pace. The growth in this field, unlike most established fields, has been both in breadth and depth of concepts and techniques. Machine vision techniques are being applied in areas ranging from medical imaging to remote sensing, industrial inspection to document processing and nanotechnology to multimedia databases. The goal of a machine vision system is to create a model of the real world from images. Computer vision recognition has attracted the attention of researchers in many application areas and has been used to solve many ranges of problems. The purpose of this paper is to consider recognition of objects manufactured in mechanical industry. Mechanically manufactured parts have recognition difficulties due to manufacturing process including machine malfunctioning, tool wear and variations in raw material. This paper considers the problem of recognizing and classifying the objects of such parts. RGB images of five objects are used as an input. The Fourier descriptor technique is used for recognition of objects. Artificial neural network (ANN) is used for classification of five different objects. These objects are kept in different orientations for invariant rotation, translation and scaling. The feed forward neural network with back-propagation learning algorithm is used to train the network. This paper shows the effect of different network architecture and numbers of hidden nodes on the classification accuracy of objects.

The overall goal of this research is to develop algorithms for feature-based recognition of 2D parts from intensity images. Most present industrial vision systems are custom-designed systems, which can only handle a specific application. This is not surprising, since different applications have different geometry, different reflectance properties of the parts.

Classification accuracy is affected by the changing network architecture. ANN is computationally demanding and slow. A total of 20 hidden nodes network structure produced the best results at 500 iterations (90 percent accuracy based on overall accuracy and 87.50 percent based on κ coefficient). So, 20 hidden nodes are selected for further analysis. The learning rate is set to 0.1, and momentum term used is 0.2 that give the best results architectures. The confusion matrix also shows the accuracy of the classifier. Hence, with these results the proposed system can be used efficiently for more objects.

After calculating the variation of overall accuracy with different network architectures, the results of different configuration of the sample size of 50 testing images are taken. Table II shows the results of the confusion matrix obtained on these testing samples of objects.

The purpose of this paper is to develop an integrated, computer vision-based system to operate a commercial wheelchair-mounted robotic manipulator (WMRM). In addition, a gesture recognition interface system was developed specially for individuals with upper-level spinal cord injuries including object tracking and face recognition to function as an efficient, hands-free WMRM controller.

Two Kinect® cameras were used synergistically to perform a variety of simple object retrieval tasks. One camera was used to interpret the hand gestures and locate the operator's face for object positioning, and then send those as commands to control the WMRM. The other sensor was used to automatically recognize different daily living objects selected by the subjects. An object recognition module employing the Speeded Up Robust Features algorithm was implemented and recognition results were sent as a commands for “coarse positioning” of the robotic arm near the selected object. Automatic face detection was provided as a shortcut enabling the positing of the objects close by the subject's face.

The gesture recognition interface incorporated hand detection, tracking and recognition algorithms, and yielded a recognition accuracy of 97.5 percent for an eight-gesture lexicon. Tasks’ completion time were conducted to compare manual (gestures only) and semi-manual (gestures, automatic face detection, and object recognition) WMRM control modes. The use of automatic face and object detection significantly reduced the completion times for retrieving a variety of daily living objects.

Integration of three computer vision modules were used to construct an effective and hand-free interface for individuals with upper-limb mobility impairments to control a WMRM.

Recognising the as-built state of construction elements is crucial for construction progress monitoring. Construction scholars have used computer vision-based algorithms to automate this process. Robust object recognition from indoor site images has been inhibited by technical challenges related to indoor objects, lighting conditions and camera positioning. Compared with traditional machine learning algorithms, one-stage detector deep learning (DL) algorithms can prioritise the inference speed, enable real-time accurate object detection and classification. This study aims to present a DL-based approach to facilitate the as-built state recognition of indoor construction works.

The one-stage DL-based approach was built upon YOLO version 4 (YOLOv4) algorithm using transfer learning with few hyperparameters customised and trained in the Google Colab virtual machine. The process of framing, insulation and drywall installation of indoor partitions was selected as the as-built scenario. For training, images were captured from two indoor sites with publicly available online images.

The DL model reported a best-trained weight with a mean average precision of 92% and an average loss of 0.83. Compared to previous studies, the automation level of this study is high due to the use of fixed time-lapse cameras for data collection and zero manual intervention from the pre-processing algorithms to enhance visual quality of indoor images.

This study extends the application of DL models for recognising as-built state of indoor construction works upon providing training images. Presenting a workflow on training DL models in a virtual machine platform by reducing the computational complexities associated with DL models is also materialised.

The digital construction transformation requires using emerging digital technology such as deep learning to automate implementing tasks. Therefore, this paper aims to evaluate the current state of using deep learning in the construction management tasks to enable researchers to determine the capabilities of current solutions, as well as finding research gaps to carry out more research to bridge revealed knowledge and practice gaps.

The scientometric analysis is conducted for 181 articles to assess the density of publications in different topics of deep learning-based construction management applications. After that, a thematic and gap analysis are conducted to analyze contributions and limitations of key published articles in each area of application.

The scientometric analysis indicates that there are four main applications of deep learning in construction management, namely, automating progress monitoring, automating safety warning for workers, managing construction equipment, integrating Internet of things with deep learning to automatically collect data from the site. The thematic and gap analysis refers to many successful cases of using deep learning in automating site management tasks; however, more validations are recommended to test developed solutions, as well as additional research is required to consider practitioners and workers perspectives to implement existing applications in their daily tasks.

This paper enables researchers to directly find the research gaps in the existing solutions and develop more workable applications to bridge revealed gaps. Accordingly, this will be reflected on speeding the digital construction transformation, which is a strategy over the world.

To the best of the authors’ knowledge, this paper is the first of its kind to adopt a structured technique to assess deep learning-based construction site management applications to enable researcher/practitioners to either adopting these applications in their projects or conducting further research to extend existing solutions and bridging revealed knowledge gaps.

The purpose of this paper is to present a literature review of laser scanning and 3D modelling devices, modes of delivery and applications within the architecture, engineering, construction and owner-operated sector. Such devices are inextricably linked to modern digital built environment practices, particularly when used in conjunction with as-built building information modelling (BIM) development. The research also reports upon innovative technological advancements (such as machine vision) that coalesce with 3D scanning solutions.

A synthesis of literature is used to develop: a hierarchy of the modes of delivery for laser scan devices; a thematic analysis of 3D terrestrial laser scan technology applications; and a componential cross-comparative tabulation of laser scan technology and specifications.

Findings reveal that the costly and labour intensive attributes of laser scanning devices have stimulated the development of hybrid automated and intelligent technologies to improve performance. Such developments are set to satisfy the increasing demand for digitisation of both existing and new buildings into BIM. Future work proposed will seek to: review what coalescence of digital technologies will provide an optimal and cost-effective solution to accurately re-constructing the digital built environment; conduct case studies that implement hybrid digital solutions in pragmatic facilities management scenarios to measure their performance and user satisfaction; and eliminate manual remodelling tasks (such as point cloud reconstruction) via the use of computational intelligence algorithms integral within cloud-based BIM platforms.

Although laser scanning and 3D modelling have been widely covered en passant within the literature, scant research has conducted a holistic review of the technology, its applications and future developments. This review presents concise and lucid reference guidance that will intellectually challenge, and better inform, both practitioners and researchers.

In Hong Kong, over 20,000 private residential buildings will be 50 plus years old by 2039. However, building maintenance has not been owners’ popular interest because of the high cost as well as the complexities in justifying whether the quantities and prices of the maintenance works are reasonable. This paper therefore aims to validate the practicality of adopting Scan-to-BIM: Terrestrial Laser Scan (TLS) and Building Information Modelling (BIM) to perform quantity take-offs (QTO) for estimating building maintenance costs.

A 64-year-old tenement building was selected to conduct a case study. In this instance, the building had undergone a Scan-to-BIM survey approach to generate QTO for the bills of quantities for external painting works. The Scan-to-BIM approach includes site visit, positioning of scanning equipment, assignment of circular scan routes, point cloud registration and identification of residual error. After that, time, cost and quality data were logged into contrast with QTO on as-built plans for external wall plastering works.

The “time”, “cost” and “quality” of the Scan-to BIM practice were then examined and compared with the prevailing practices of manual measurements on as-built drawings. As noted from the results, the initial cost of Scan-to BIM is high, owing to the cost of equipment, software and capable available operators. However, the authors identified that the time and cost can be significantly minimised by developing and implementing efficient practices such as preparing a detailed scan plan, equipping modeller with quantity surveying knowledge, using automated object recognition and 5D BIM software packages such as Vico Office and CostX.

The upshot is that Scan-to-BIM could be one of the measures to advance the clarity in the QTO and estimated price of the maintenance projects.

The practicability of Scan-to-BIM has received limited attention on existing building maintenance project. The Scan-to-BIM approach was examined using a case building of a 64-year-old tenement building. The approach demonstrated in this research study is promised to advance the clarity in the QTO and estimated price of maintenance project.

The purpose of this paper is to present a novel microbiological lab robot that facilitates high through‐put sample preparation for rapid state‐of‐the‐art identification.

Development concentrated on two main points: research initially focused on various methods for picking a micro‐organism colony from a petri dish without any medium adhering; and subsequently on completely documenting sample handling with little effort.

A sensorless system for picking micro‐organisms from a petri dish was engineered and prototyped. A documented process in the demonstrator demonstrates its usability even for certified clinical operations.

The handling of solid phase biological objects is only in its infancy. This research focused on the preparation of samples from micro‐organism colonies for MALDI‐TOF MS. A specific type of gripper was developed to do this. The handling of other biological objects, e.g. from cell cultures or intermediate stages of tissue engineering, is still a largely open field for future research.

New analysis methods often only become accepted when the preparatory processes are also taken into account – highly parallel operations (e.g. MALDI‐TOF MS) are particularly impractical for humans and difficult for data handling to manage. Given the specific demands, only an interdisciplinary team can adapt the automation engineering successfully.

This paper presents an approach to and implementation of the automation of manual operations in biotechnology. It is intended to encourage health professionals, biologists and engineers to jointly research and interdisciplinarily automate complex operations.

The purpose of this paper is to explore the potential and adoption of closed-circuit television (CCTV) surveillance-based security system (hereafter “CCTV”) for enhancing the security of library materials in academic libraries of universities (central, state, deemed and private) and prestigious institutions such as Indian Institutes of Technology and Indian Institutes of Management in a developing country, i.e. India. The study also overviewed the CCTV policies of the studied libraries of universities/institutions as they relate to the ethical aspects of the surveillance system.

Structured questionnaire was designed and distributed among librarians of 24 academic libraries covering each zone of India in October 2019 in both physical and online manner. All 24 filled-in questionnaires were collected personally and online by the investigator were found valid eliciting a response rate of 100%. All the 24 filled-in questionnaires were included in the analysis of the interpretation of data. The response to 18 questions was analyzed in the form of tables and figures using descriptive statistical methods.

The study reveals that librarians’ found CCTV useful for security by controlling theft, unethical losses and missing items. It also helped to curb mutilation and vandalism, procurement of the rare material via the latest camera devices and night vision capturing, besides improving the service efficiency of the patron, as well as staff. The quantitative study surveyed security professionals to assess how each university/institution developed, deployed and integrated CCTV policies related to securing video data, safeguarding privacy and prevention of the potential for the unethical use of surveillance cameras. The analysis of the survey responses determined that more than 50% of the universities/institutions participating had a written CCTV policy. Further, library professionals find that the future of libraries lies in a CCTV system, so the cost should be brought down to improve return on investment by the mass adoption of this technology in a developing country such as India.

The findings of the study showed that the potential uses of CCTV in Indian libraries are slow compared to that of the libraries of developed countries and some of the developing countries. Many of the CCTV policies that universities/institutions did have failed to include mandated training of personnel or provisions ensuring that their policies remained up-to-date. It is suggested that universities and institutions understudy should realize the benefits of CCTV systems and incorporate-related updated tools in the security and multi-purpose uses in the libraries to enhance the services for the users and security for the materials or collections.

The paper includes implications for libraries and their professionals to approach CCTV systems with ethical considerations for procurement of library collections, which help to detect mutilation/theft, observe the misbehavior of users, as well as staff and deployment, should not be decided merely while balancing security demands.

The study is significant because it represents one of the earliest works to shed light on the current level of the use of CCTV system by librarians of studied libraries of universities/institutes in developing country such as India and how they are providing CCTV-based security and services, which are currently in its primitive nature. The study also suggested that select libraries are required to weigh up and balance many competing desires, demands and objectives.

This paper provides a concise overview of the various applications/area and uses of CCTV system including its procedures during implementation, merits and demerits while using the system described above in libraries and recommends this technology to other libraries for faster and better services for their users and security to their library materials in today’s technological advancement. It provides a set of issues that should be considered before system adoption or deployment.

In this paper, we review the process of “3D modeling” and “model‐based recognition” along with their potential industrial applications. We put a particular emphasis on the case scenario of robot grasp analysis for which 3D model‐based object recognition seems to be a more palpable choice compared with the conventional tactile sensors solutions. We also put a particular emphasis on the main challenges in the areas of 3D modeling and model‐based recognition and give a brief literature review of the latest research that was carried out to respond to these challenges.