Assessment of Convolutional Neural Networks for Asset Detection in Dynamic Automation Construction Environments

Simple item page
dc.catalogadorjwg
dc.contributor.authorGuaman-Rivera R.
dc.contributor.authorMenendez, Osvaldo
dc.contributor.authorArevalo Ramírez, Tito Andre
dc.contributor.authorAro K.
dc.contributor.authorPrado A.
dc.contributor.authorGarcia-Alvarado R.
dc.contributor.authorAuat-Cheein F.
dc.date.accessioned2025-03-18T19:11:42Z
dc.date.available2025-03-18T19:11:42Z
dc.date.issued2023
dc.description.abstractIntegrating social robotics into the construction industry, particularly in the context of Industry 5.0, faces several challenges in creating complex environments that seamlessly blend human and machine interactions. In this regard, the emergence of intelligent and expert systems holds promising technologies to enhance construction tasks focused on robots and workers in 3D printing applications. This work compares several methods of convolutional neural network-based object detectors designed to identify distinct construction assets and workers within the dynamic environment of 3D printing. To this end, different versions of the You Only Look Once v8 (YOLO v8) algorithm have been implemented, trained, and experimentally tested using several images captured within dynamic construction environments. Furthermore, we present an in-depth comparison between YOLO v8 and its preceding versions, namely YOLO v7 and YOLO v5. Experimental results disclosed the high performance of the proposed approach in effectively detecting three distinct entities (workers, robotic platforms, and building elements), achieving a precision rate of up to 98.8%.
dc.fuente.origenORCID
dc.identifier.doi10.1109/CHILECON60335.2023.10418631
dc.identifier.issn28321537 28321529
dc.identifier.scopusidSCOPUS_ID:85189507319
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/102769
dc.information.autorucEscuela de Ingeniería; Arevalo Ramírez, Tito Andre; 0000-0003-2542-6545; 1300544
dc.language.isoen
dc.nota.accesoContenido parcial
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.revistaProceedings - IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, ChileCon
dc.rightsacceso restringido
dc.subject3D printing
dc.subjectAutomation in construction
dc.subjectconvolutional neural network
dc.subjectrobotics
dc.subject.ddc620
dc.subject.deweyIngenieríaes_ES
dc.titleAssessment of Convolutional Neural Networks for Asset Detection in Dynamic Automation Construction Environments
dc.typecomunicación de congreso
sipa.codpersvinculados1300544
sipa.trazabilidadORCID;2025-03-03
Files
Collections
Artículos de conferencia