Developing a Method to Detect Indoor Spatial Entities in Omnidirectional Images for Constructing IndoorGML Data

The continuous increase in interest in indoor space demands the development of LBSs (Location Based Services) for navigation, routing, and space query in these environments. Applications providing these services must use visualization and topology data together to provide relevant services to users. Applications have frequently used geometric object-based visualization models for indoor space LBSs. These have the advantage of being easy to use with IndoorGML, an indoor topology model, but have the disadvantage of high construction cost and heavy data. Correspondingly, the image-based visualization model drew attention as an alternative model. However, using such models requires identifying objects on the image to be used alongside IndoorGML, presenting a limitation because it is difficult to identify objects in the image pixels directly. To overcome the limitations of image-based visualization models and reconsider the usability of indoor space LBS using images, this study presents a method to automatically detect spatial objects required to construct IndoorGML from images using deep learning. This methodology aims to detect objects mainly used in indoor LBSs, which include door and stair objects portrayed in indoor omnidirectional images. This study proposes a detailed method of constructing a training dataset for indoor spatial object detection. Moreover, this research presents a method of refining the training dataset and directly acquiring omnidirectional image data to train it as an object detection model that can be used universally in various buildings while maintaining compliant accuracy.