VDC en la implementación de un modelo de inteligencia artificial para la supervisión remota de obra CEBUL al 65% de desarrollo

El siguiente proyecto tienen como objetivo entregar un proyecto de investigación y artículo científico donde se desarrolle un modelo de Inteligencia Artificial tomando como referencia el Centro de Bienestar Universitario. Se aplicará visión computacional para detectar personas y maquinaria en obra, e identificar el avance de la estructura comparando el BIM 4D con las imágenes de obra. Esta información será de utilidad para la supervisión del avance de obra por parte de la Oficina de Infraestructura y la constructora. El proyecto tiene como duración 1 año

Dataset of manually classified images obtained from a construction site

A manually classified dataset of images obtained by four static cameras located around a construction site is presented. Eight object classes, typically found in a construction environment, were considered. The dataset consists of 1046 images selected from video footage by a frame extraction algorithm and txt files containing the objects' class and coordinates information. These data can be used to develop computer vision techniques in the engineering and construction fields

Using UAVs and Photogrammetry in Bathymetric Surveys in Shallow Waters

Indexado en ScopusThe use of UAV (unmanned aerial vehicle) platforms and photogrammetry in bathymetric surveys has been established as a technological advancement that allows these activities to be conducted safely, more affordably, and at higher accuracy levels. This study evaluates the error levels obtained in photogrammetric UAV flights, with measurements obtained in surveys carried out in a controlled water body (pool) at different depths. We assessed the relationship between turbidity and luminosity factors and how this might affect the calculation of bathymetric survey errors using photogrammetry at different shallow-water depths. The results revealed that the highest luminosity generated the lowest error up to a depth of 0.97 m. Furthermore, after assessing the variations in turbidity, the following two situations were observed: (1) at shallower depths (not exceeding 0.49 m), increased turbidity levels positively contributed error reduction; and (2) at greater depths (exceeding 0.49 m), increased turbidity resulted in increased errors. In conclusion, UAV-based photogrammetry can be applied, within a known margin of error, in bathymetric surveys on underwater surfaces in shallow waters not exceeding a depth of 1 m.Revisión por pare

Albergue para menores en estado de abandono y cuna- jardin en Lurín: el Mat-building como herramienta de diseño en arquitectura infantil

Trabajo de suficiencia profesionalDiseñar un proyecto en el cual la arquitectura pueda ser una herramienta que permita el desarrollo y recuperación del menor en estado de abandono, además a través de la diversificación espacial que tendrá el proyecto se busca promover la interacción social de los menores con personas ajenas al albergue, esto a través de una cuna-jardín y programas de talleres para el uso de la comunidad

Dataset of manually classified images obtained from a construction site

A manually classified dataset of images obtained by four static cameras located around a construction site is presented. Eight object classes, typically found in a construction environment, were considered. The dataset consists of 1046 images selected from video footage by a frame extraction algorithm and txt files containing the objects' class and coordinates information. These data can be used to develop computer vision techniques in the engineering and construction field

Artificial Intelligence Applied to the Control and Monitoring of Construction Site Personnel

Many countries are working towards gradually lifting restrictions generated by the COVID-19 virus as post-quarantine measures. The construction industry has had to adapt to new forms of work with economic and physical restrictions. For physical restrictions, the most worrying one is the risk of contagion, as many studies have indicated that social distancing is one of the most effective biosecurity measures. In this research, a training process was executed on a neural network to ensure an adequate social distance policy in a construction environment to identify people inside construction sites. More specific training was carried out to identify people performing activities in a position other than being completely upright, as is usually the case with construction workers. The “You Only Look Once” (YOLO) version 4 algorithm was used to train 2 classes of objects, an upright person and a crouched person. More than one thousand images of a construction site were used as a data set, achieving an accuracy of 77.98%. This research presents the results and recommendations to detect the people and calculate the distance between them. Based on the distance calculation, an alert report can be generated for the work areas for the health and safety team to take preventive actions

Using UAVs and Photogrammetry in Bathymetric Surveys in Shallow Waters

The use of UAV (unmanned aerial vehicle) platforms and photogrammetry in bathymetric surveys has been established as a technological advancement that allows these activities to be conducted safely, more affordably, and at higher accuracy levels. This study evaluates the error levels obtained in photogrammetric UAV flights, with measurements obtained in surveys carried out in a controlled water body (pool) at different depths. We assessed the relationship between turbidity and luminosity factors and how this might affect the calculation of bathymetric survey errors using photogrammetry at different shallow-water depths. The results revealed that the highest luminosity generated the lowest error up to a depth of 0.97 m. Furthermore, after assessing the variations in turbidity, the following two situations were observed: (1) at shallower depths (not exceeding 0.49 m), increased turbidity levels positively contributed error reduction; and (2) at greater depths (exceeding 0.49 m), increased turbidity resulted in increased errors. In conclusion, UAV-based photogrammetry can be applied, within a known margin of error, in bathymetric surveys on underwater surfaces in shallow waters not exceeding a depth of 1 m

Integrating a LiDAR Sensor in a UAV Platform to Obtain a Georeferenced Point Cloud

The combination of light detection and ranging (LiDAR) sensors and unmanned aerial vehicle (UAV) platforms have garnered considerable interest in recent years because of the wide range of applications performed through the generation of point clouds, such as surveying, building layouts and infrastructure inspection. The attributed benefits include a shorter execution time and higher accuracy when surveying and georeferencing infrastructure and building projects. This study seeks to develop, integrate and use a LiDAR sensor system implemented in a UAV to collect topography data and propose a procedure for obtaining a georeferenced point cloud that can be configured according to the user’s needs. A structure was designed and built to mount the LiDAR system components to the UAV. Survey tests were performed to determine the system’s accuracy. An open-source ROS package was used to acquire data and generate point clouds. The results were compared against a photogrammetric survey, denoting a mean squared error of 17.1 cm in survey measurement reliability and 76.6 cm in georeferencing reliability. Therefore, the developed system can be used to reconstruct extensive topographic environments and large-scale infrastructure in which a presentation scale of 1/2000 or more is required, due to the accuracy obtained in the work presented

Integrating a LiDAR Sensor in a UAV Platform to Obtain a Georeferenced Point Cloud

The combination of light detection and ranging (LiDAR) sensors and unmanned aerial vehicle (UAV) platforms have garnered considerable interest in recent years because of the wide range of applications performed through the generation of point clouds, such as surveying, building layouts and infrastructure inspection. The attributed benefits include a shorter execution time and higher accuracy when surveying and georeferencing infrastructure and building projects. This study seeks to develop, integrate and use a LiDAR sensor system implemented in a UAV to collect topography data and propose a procedure for obtaining a georeferenced point cloud that can be configured according to the user’s needs. A structure was designed and built to mount the LiDAR system components to the UAV. Survey tests were performed to determine the system’s accuracy. An open-source ROS package was used to acquire data and generate point clouds. The results were compared against a photogrammetric survey, denoting a mean squared error of 17.1 cm in survey measurement reliability and 76.6 cm in georeferencing reliability. Therefore, the developed system can be used to reconstruct extensive topographic environments and large-scale infrastructure in which a presentation scale of 1/2000 or more is required, due to the accuracy obtained in the work presented