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

Sistema de incremento de vocabulario para la mejora de la comprensión lectora en primaria con ayuda de realidad aumentada

Peru has achieved unsatisfactory results in international and national reading assessments. In the 2015 Programme for International Student Assessment (PISA), Peru obtained 398 points, almost 100 points below the average. The country ranked 64 out of 72 participants. In the national ECE assessment, the higher the school grade, the less the achievement percentage of academic goals. Said percentage accounts for 46.4% and 31.4% in the 2nd and 4th grade of primary school, respectively, and 14.3% in the 2nd grade of secondary school. The factor that causes this problem is a poor vocabulary.El Perú presenta resultados insatisfactorios en las evaluaciones internacionales y nacionales en el área de lectura. En los resultados del Programa para la Evaluación Internacional de Alumnos (PISA) del año 2015, el Perú obtuvo 398 puntos, casi 100 puntos por debajo de la media. El país se posicionó en el puesto 64 de los 72 participantes. En la evaluación nacional ECE, a medida que los grados avanzan el porcentaje de cumplimiento de objetivos académicos van decayendo. Pasa de 46,4 %, 31,4 % y 14,3 %, en el segundo y cuarto grado de primaria, y segundo de secundaria, respectivamente. El factor por acatar dentro del problema de la baja comprensión lectora es el vocabulario reducido

Detection and Evaluation of Construction Cracks through Image Analysis Using Computer Vision

The introduction of artificial intelligence methods and techniques in the construction industry has fostered innovation and constant improvement in the automation of monitoring and control processes at construction sites, although there are areas where more studies still need to be conducted. This paper proposes a method to determine the criticality of cracks in concrete samples. The proposed method uses a previously trained YOLOv4 neural network to identify concrete cracks. Then, the region of interest, determined by the bounding box resulting from the neural network model classification, is extracted. Finally, the extracted image is converted to negative grayscale to quantify the number of white pixels above a certain threshold, automatically allowing the system to characterize the fracture’s extent and criticality. The classification module reached a veracity between 98.36% and 99.75% when identifying five concrete crack types of failures in 1132 images. A qualitative analysis of the results obtained from the characterization module shows a promising alternative to evaluate the criticality of concrete cracks. © 2023 by the authors

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