Print Email Facebook Twitter Deep Learning Object-Recognition in a Design-to-Robotic-Production and -Operation Implementation Title Deep Learning Object-Recognition in a Design-to-Robotic-Production and -Operation Implementation Author Liu Cheng, Alexander (TU Delft Digital Architecture; GRAFT Gesellschaft von Architekten) Bier, H.H. (TU Delft Digital Architecture; Anhalt University of Applied Sciences Dessau) Mostafavi, Sina (TU Delft Digital Architecture; Anhalt University of Applied Sciences Dessau) Date 2017 Abstract This paper presents a new instance in a series of discrete proof-of-concept implementations of comprehensively intelligent built-environments based on Design-to-Robotic-Production and -Operation (D2RP&O) principles developed at Delft University of Technology (TUD). With respect to D2RP, the featured implementation presents a customized design-to-production framework informed by optimization strategies based on point clouds. With respect to D2RO, said implementation builds on a previously developed highly heterogeneous, partially meshed, self-healing, and Machine Learning (ML) enabled Wireless Sensor and Actuator Network (WSAN). In this instance, a computer vision mechanism based on open-source Deep Learning (DL) / Convolutional Neural Networks (CNNs) for object-recognition is added to the inherited ecosystem. This mechanism is integrated into the system’s Fall-Detection and -Intervention System in order to enable decentralized detection of three types of events and to instantiate corresponding interventions. The first type pertains to human-centered activities / accidents, where cellular- and internet-based intervention notifications are generated in response. The second pertains to object-centered events that require the physical intervention of an automated robotic agent. Finally, the third pertains to object-centered events that elicit visual / aural notification cues for human feedback. These features, in conjunction with their enabling architectures, are intended as essential components in the on-going development of highly sophisticated alternatives to existing Ambient Intelligence (AmI) solutions. Subject RobotsObject recognitionCamerasShapeVisualizationConcreteMachine learning To reference this document use: http://resolver.tudelft.nl/uuid:af0882e3-36a0-4ce9-8dd7-c53c935e9f88 DOI https://doi.org/10.1109/ETCM.2017.8247495 Publisher IEEE ISBN 978-1-5386-3894-1 Source Proceedings of the 2nd IEEE Ecuador Technical Chapters Meeting (ETCM 2017) Event ETCM 2017: 2nd IEEE Ecuador Technical Chapters Meeting 2017, 2017-10-18 → 2017-10-20, Hotel Barceló, Salinas, Ecuador Bibliographical note Accepted Author Manuscript Part of collection Institutional Repository Document type conference paper Rights © 2017 Alexander Liu Cheng, H.H. Bier, Sina Mostafavi Files PDF ETCM2017_ALC_HHB_SM_ver11_.pdf 506.61 KB Close viewer /islandora/object/uuid:af0882e3-36a0-4ce9-8dd7-c53c935e9f88/datastream/OBJ/view