Pubblicazione scientifica

Finding divers with SCUBANet

FILONE TECNOLOGICO	TEMA
sicurezza e qualità sui luoghi di lavoro

RIASSUNTO

Robot-diver communication underwater is complicated by the attenuation of RF signals, the complexities of the environment in terms of deploying interaction devices, and issues related to the cognitive loading of human operators. Humans operating underwater have developed a simple yet effective strategy for diver-diver communication based on the visual recognition of gestures. Can a similar approach be effective for diver-robot communication? Here we present experiments with SCUBANet, an underwater detection dataset of body parts associated with diver-robot communication. Given the nature of standard diver gestures, here we concentrate on diver recognition and in particular on diver body-head-hand localization and examine the feasibility of using a CNN-based approach to address this problem. Such data-driven approaches typically require an appropriately annotated dataset. The SCUBANet dataset contains images of object classes commonly encountered during human-robot communication underwater. Object classes are labeled using per-instance bounding boxes. Annotations were created through crowd sourcing via a web-based interface to ease deployment. We provide baseline performance on diver and diver component recognition and localization using transfer learning on three widely available pre-trained models.

DATA

Data di pubblicazione: 01/05/2019

AUTORI

ROBERT CODD-DOWNEY
MICHAEL JENKIN

ENTE DI AFFERENZA

DEPT ELEC ENG & COMPUTER SCIENCE YORK UNIV TORONTO ONTARIO M3J 1P3 CANADA

RIVISTA

2019 International Conference on Robotics and Automation (ICRA) (Page(s): 5746-5751)

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