Image Captioning and Classification of Dangerous Situations

by   Octavio Arriaga, et al.
Hochschule Bonn-Rhein-Sieg
Heriot-Watt University

Current robot platforms are being employed to collaborate with humans in a wide range of domestic and industrial tasks. These environments require autonomous systems that are able to classify and communicate anomalous situations such as fires, injured persons, car accidents; or generally, any potentially dangerous situation for humans. In this paper we introduce an anomaly detection dataset for the purpose of robot applications as well as the design and implementation of a deep learning architecture that classifies and describes dangerous situations using only a single image as input. We report a classification accuracy of 97 dataset publicly available after this paper is accepted.


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