Image Captioning and Classification of Dangerous Situations
This addresses the need for autonomous systems to detect anomalies like fires or accidents in human-robot collaboration environments, though it is incremental as it builds on existing deep learning methods.
The paper tackles the problem of enabling robots to classify and describe dangerous situations from single images, achieving 97% classification accuracy and a METEOR score of 16.2.
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.