Orientation-Constrained System for Lamp Detection in Buildings Based on Computer Vision

Computer vision is used in this work to detect lighting elements in buildings with the goal of improving the accuracy of previous methods to provide a precise inventory of the location and state of lamps. Using the framework developed in our previous works, we introduce two new modifications to enhance the system: first, a constraint on the orientation of the detected poses in the optimization methods for both the initial and the refined estimates based on the geometric information of the building information modelling (BIM) model; second, an additional reprojection error filtering step to discard the erroneous poses introduced with the orientation restrictions, keeping the identification and localization errors low while greatly increasing the number of detections. These enhancements are tested in five different case studies with more than 30,000 images, with results showing improvements in the number of detections, the percentage of correct model and state identifications, and the distance between detections and reference positions.Authors want to give thanks to the Xunta de Galicia under Grant ED481A and the Spanish Ministry of Economy and Competitiveness under the National Science Program TEC2017-84197-C4-2-R

Key functions in BIM-based AR platforms

The integration of Augmented Reality and Building Information Modelling is a promising area of research; however, fragmentation in literature hinders the development of mature BIM-based AR platforms. This paper aims to minimise the fragmentation in the literature by identifying the key functions that represent the essential capabilities of BIM-AR platforms. A systematic literature review is employed to identify, categorise, and discuss the key functions. The outcome of this paper identifies six key functions: positioning (P), interaction (I), visualisation (V), collaboration (C), automation (A), and integration (T). These key functions act as the foundation for an evaluation framework that can assist practitioners, developers, and researchers with assessing the requirements of the targeted application area, and hence be better informed on the appropriate devices, software, and techniques to use. Finally, this paper emphasises the importance of industrial-academic collaboration in BIM-AR research and suggests prospects for automation through the application of artificial intelligence

ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects

This paper presents a conceptual framework for the optimization of environmental sustainability in engineering projects, both for products and industrial facilities or processes. The main objective of this work is to propose a conceptual framework to help researchers to approach optimization under the criteria of sustainability of engineering projects, making use of current Machine Learning techniques. For the development of this conceptual framework, a bibliographic search has been carried out on the Web of Science. From the selected documents and through a hermeneutic procedure the texts have been analyzed and the conceptual framework has been carried out. A graphic representation pyramid shape is shown to clearly define the variables of the proposed conceptual framework and their relationships. The conceptual framework consists of 5 dimensions; its acronym is ADAPTS. In the base are: (1) the Application to which it is intended, (2) the available DAta, (3) the APproach under which it is operated, and (4) the machine learning Tool used. At the top of the pyramid, (5) the necessary Sensing. A study case is proposed to show its applicability. This work is part of a broader line of research, in terms of optimization under sustainability criteria.Telefónica Chair “Intelligence in Networks” of the University of Seville (Spain

Visual and Camera Sensors

This book includes 13 papers published in Special Issue ("Visual and Camera Sensors") of the journal Sensors. The goal of this Special Issue was to invite high-quality, state-of-the-art research papers dealing with challenging issues in visual and camera sensors

Fine Art Pattern Extraction and Recognition

This is a reprint of articles from the Special Issue published online in the open access journal Journal of Imaging (ISSN 2313-433X) (available at: https://www.mdpi.com/journal/jimaging/special issues/faper2020)

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Material Management Framework utilizing Near Real-Time Monitoring of Construction Operations

Materials management is a vital process in the delivery of construction facilities. Studies by the Construction Industry Institute (CII) have demonstrated that materials and installed equipment can constitute 40– 70% of the total construction hard cost and affect 80% of the project schedule. Despite its significance, most of the construction industry sectors are suffering from poor material management processes including inaccurate warehouse records, over-ordering and large surpluses of material at project completion, poor site storage practices, running out of materials, late deliveries, double-handling of components, out-of-specification material, and out of sequence deliveries which all result in low productivity, delay in construction and cost overruns. Inefficient material management can be attributed to the complex, unstructured, and dynamic nature of the construction industry, which has not been considered in a large number of studies available in this field. The literature reveals that available computer-based materials management systems focus on (1) integration of the materials management functions, and (2) application of Automated Data Collection (ADC) technologies to collect materials localization and tracking data for their computerized materials management systems. Moreover in studies that focused on applying ADC technologies in construction materials management, positioning and tracking critical resources in construction sites, and identifying unique materials received at the job site are the main applications of their used technologies. Even though, various studies have improved materials management processes copiously in the construction industry, the benefits of considering the dynamic nature of construction (in terms of near real-time progress monitoring using state of the art technologies and techniques) and its integration with a dynamic materials management system have been left out. So, in contrast with other studies, this research presents a construction materials management framework capable of considering the dynamic nature of construction projects. It includes a vital component to monitor project progress in near real-time to estimate the installation and consumption of materials. This framework consists of three models: “preconstruction model,” “construction model,” and “data analysis and reporting model.” This framework enables (1) generation of optimized material delivery schedules based on Material Requirement Planning (MRP) and minimum total cost, (2) issuance of material Purchase Orders (POs) according to optimized delivery schedules, (3) tracking the status of POs (Expediting methods), (4) collection and assessment of material data as it arrives on site, (5) considering the inherent dynamics of construction operations by monitoring project progress to update project schedule and estimate near real-time consumption of materials and eventually (6) updating MRP and optimized delivery schedule frequently throughout the construction phase. An optimized material delivery schedule and an optimized purchase schedule with the least cost are generated by the preconstruction model to avoid consequences of early/late purchasing and excess/inadequate purchasing. Accurate assessment of project progress and estimation of installed or consumed materials are essential for an effective construction material management system. The construction model focuses on the collection of near real-time site data using ADC technologies. Project progress is visualized from two different perspectives, comparing as-built with as-planned and comparing various as-built status captured on consecutive points of time. Due to the recent improvements in digital photography and webcams, which made this technology more cost-effective and practical for monitoring project progress, digital imaging (including 360° images) is selected and applied for project progress monitoring in the construction (data acquisition) model. In the last model, which is the data analysis and reporting model, Deep Learning (DL) and image processing algorithms are proposed to visualize and detect actual progress in terms of built elements in near real-time. In contrast with the other studies in which conventional computer vision algorithms are often used to monitor projects progress, in this research, a deep Convolutional Auto-Encoder (CAE) and Mask Region-based Convolutional Neural Network (R-CNN) are utilized to facilitate vision-based indoor and outdoor progress monitoring of construction operations. The updated project schedule based on the actual progress is the output of this model, and it is used as the primary input for the developed material management framework to update MRP, optimized material delivery, and purchase schedules, respectively. Applicability of the models in the developed material management framework has been tested through laboratory and field experiments. The results demonstrated the accuracy and capabilities of the developed models in the framework