ODROM: Object Detection and Recognition supported by Ontologies and applied to Museums

In robotics, object detection in images or videos, obtained in real-time from sensors of robots can be used to support the implementation of service robot tasks (e.g., navigation, model its social behavior, recognize objects in a specific domain), usually accomplished in indoor environments. However, traditional deep learning based object detection techniques present limitations in such indoor environments, specifically related to the detection of small objects and the management of high density of multiple objects. Coupled with these limitations, for specific domains (e.g., hospitals, museums), it is important that the robot, apart from detecting objects, extracts and knows information of the targeted objects. Ontologies, as a part of the Semantic Web, are presented as a feasible option to formally represent the information related to the objects of a particular domain. In this context, this work proposes an object detection and recognition process based on a Deep Learning algorithm, object descriptors, and an ontology. ODROM, an Object Detection and Recognition algorithm supported by Ontologies and applied to Museums, is an implementation to validate the proposal. Experiments show that the usage of ontologies is a good way of desambiguating the detection, obtained with a and $\mathbf{mAP}{@}0.5=0.88$ and a $\mathbf{mAP}{@}[0.5:0.95]=61\%$.