A Novel Dataset For Intelligent Indoor Object Detection Systems

mouna Afif (Faculty of sciences of Monastir)
Riadh Ayachi (University of Monastir)
Yahia said (University of Monastir)
Edwige Pissaloux (University of Rouen Normandy)
Mohamed Atri (University of Monastir)

Abstract


Indoor Scene understanding and indoor objects detection is a complex high-level task for automated systems applied to natural environments. Indeed, such a task requires huge annotated indoor images to train and test intelligent computer vision applications. One of the challenging questions is to adopt and to enhance technologies to assist indoor navigation for visually impaired people (VIP) and thus improve their daily life quality. This paper presents a new labeled indoor object dataset elaborated with a goal of indoor object detection (useful for indoor localization and navigation tasks). This dataset consists of 8000 indoor images containing 16 different indoor landmark objects and classes. The originality of the annotations comes from two new facts taken into account: (1) the spatial relationships between objects present in the scene and (2) actions possible to apply to those objects (relationships between VIP and an object).This collected dataset presents many specifications and strengths as it presents various data under various lighting conditions and complex image background to ensure more robustness when training and testing objects detectors. The proposed dataset, ready for use, provides 16 vital indoor object classes in order to contribute for indoor assistance navigation for VIP.


Keywords


Indoor object detection and recognition; Indoor image dataset; Visually Impaired People (VIP); Idoor navigation

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DOI: https://doi.org/10.30564/aia.v1i1.925

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