TY  - JOUR
U1  - Zeitschriftenartikel, wissenschaftlich - nicht begutachtet (unreviewed)
A1  - Arriaga, Octavio
A1  - Plöger, Paul
A1  - Valdenegro-Toro, Matias
T1  - Image Captioning and Classification of Dangerous Situations
N2  - 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 % and METEOR score of 16.2. We will make the dataset publicly available after this paper is accepted.
U6  - https://doi.org/10.48550/arXiv.1711.02578
DO  - https://doi.org/10.48550/arXiv.1711.02578
AX  - 1711.02578
SP  - 6
S1  - 6
PB  - arXiv
ER  -