A Yolo-NAS-based approach for door and door handle detection and integration of a euclidean geometric model for grip point localization : an application in robot navigation
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Mostrar el registro completo del ítemcomunitat-uji-handle:10234/158176
comunitat-uji-handle2:10234/71345
comunitat-uji-handle3:10234/141145
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Título
A Yolo-NAS-based approach for door and door handle detection and integration of a euclidean geometric model for grip point localization : an application in robot navigationAutoría
Tutor/Supervisor; Universidad.Departamento
Marín Prades, Raúl; Universitat Jaume I. Departament d'Enginyeria i Ciència dels ComputadorsFecha de publicación
2024-02-27Editor
Universitat Jaume IResumen
Object detection and localizat,ion play a significant role in artificial intelligence, as they facilitate understanding of the surrounding environment. While archi- tectures designed for this purpose have proven ... [+]
Object detection and localizat,ion play a significant role in artificial intelligence, as they facilitate understanding of the surrounding environment. While archi- tectures designed for this purpose have proven promising and continue to advance, certain objects, such as doors and door handles, have not been extensively explored. Recognizing these specific objects is crucial for autonomous decision- making, especially in robotics, enabling safe and efficient interaction in dynamic environments like hospitals. Decision-making regarding particular objects, such as doors and their handles, involves the robot. executing specific actions, such as opening a door. However, achieving this objective goes beyond merely identifying the object; the robot needs information on how to interact with it. In the case of handles, this involves indicating to the robot the specific grip point, to open the door. In this study, the novel YOLO NAS architecture, unexplored in these objects, was trained. The results demonstrated remarkable effectiveness in detecting true positives, with a recall of 0.99. However, a lower precision was observed compared to the reference YOLO v8 version. It is noteworthy that despite the lower precision, the visual performance of the model was notable, successfully detecting doors and handles under challenging conditions of light, contrast, and other relevant considerations considered during the study. A distinctive aspect of this work is the integration of a model based on Euclidean geometry for locating the grip point. Unlike previous studies that typically place this point at the centroid of the handle, the proposed model positions it at the ends of the handle, thus leveraging effective force to open the door with potentially less effort. For the generation of this Euclidean model, the predicted bounding boxes by the YOLO NAS model serve as input. Additionally, the detection model was integrated with the Euclidean model in a robotic simulation using ROS. This implementation allowed for the identification and analysis of challenges that may arise when applying such recent architectures in robotic simulation environments. [-]
Palabras clave / Materias
Màster Universitari Erasmus Mundus en Tecnologia Geoespacial | Erasmus Mundus University Master's Degree in Geospatial Technologies | Máster Universitario Erasmus Mundus en Tecnología Geoespacial | computer vision | object detection and localization | Yolo | YOLO NAS | euclidean geometry | grip point localization | lever principle | robotics | navigation robots | hospital environments | ROS
Descripción
Treball de Final de Màster Universitari Erasmus Mundus en Tecnologia Geoespacial (Pla de 2022). Codi: SJL042. Curs acadèmic 2023-2024
Tipo de documento
info:eu-repo/semantics/masterThesisDerechos de acceso
info:eu-repo/semantics/openAccess