An Indoor Object Dataset for Mobile-based Detection and Recognition Systems for the Visually Impaired

Abstract

Indoor object detection is a challenging area of computer vision where comparatively lesser work has been done compared to its outdoor counterpart. Surely, such a task requires huge amount of training data to make any classifier detect indoor objects with high precision. This indoor object detection can become way more challenging when it has to be specifically tailored for visually impaired people’s mobility and interaction with everyday use objects. This report presents a novel indoor object dataset containing 5196 unique images of 8 everyday use indoor object category relevant to daily interaction of visually impaired people. The uniqueness of this dataset compared to existing indoor objects dataset is this dataset deals with everyday use objects and presents them with more contextual information than that is available in existing literature. Moreover, the varying lighting condition, non-canonical viewpoints, occlusion and complex background makes the dataset more robust while being trained on any object detection algorithm. Instead of going for higher accuracy we aim to find a trade-off between accuracy and speed as if this dataset is to be used to build a system for visually impaired people’s navigation needs, that system has to be deployed on mobile or sensor-based hand-held device which requires lightweight models. Hence our proposed dataset is tested on two light-weight model, namely, SSD MobileNet V2 FPNLite and EfficientDet D0. It has achieved a mean average precision (mAP) of 29.5 and 39.4 respectively on both the models which is better than the original mAP values achieved by these models. Our proposed dataset can be extended with other indoor object detection dataset, as well as it can be used to build a system for visually impaired people’s navigation.