A User Location Reset Method through Object Recognition in Indoor Navigation System Using Unity and a Smartphone (INSUS)

Network Pub Date : 2024-07-22 DOI:10.3390/network4030014
Evianita Dewi Fajrianti, Y. Panduman, Nobuo Funabiki, Amma Liesvarastranta Haz, Komang Candra Brata, S. Sukaridhoto
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Abstract

To enhance user experiences of reaching destinations in large, complex buildings, we have developed a indoor navigation system using Unity and a smartphone called INSUS. It can reset the user location using a quick response (QR) code to reduce the loss of direction of the user during navigation. However, this approach needs a number of QR code sheets to be prepared in the field, causing extra loads at implementation. In this paper, we propose another reset method to reduce loads by recognizing information of naturally installed signs in the field using object detection and Optical Character Recognition (OCR) technologies. A lot of signs exist in a building, containing texts such as room numbers, room names, and floor numbers. In the proposal, the Sign Image is taken with a smartphone, the sign is detected by YOLOv8, the text inside the sign is recognized by PaddleOCR, and it is compared with each record in the Room Database using Levenshtein distance. For evaluations, we applied the proposal in two buildings in Okayama University, Japan. The results show that YOLOv8 achieved mAP@0.5 0.995 and mAP@0.5:0.95 0.978, and PaddleOCR could extract text in the sign image accurately with an averaged CER% lower than 10%. The combination of both YOLOv8 and PaddleOCR decreases the execution time by 6.71s compared to the previous method. The results confirmed the effectiveness of the proposal.
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在使用 Unity 和智能手机的室内导航系统(INSUS)中通过物体识别重设用户位置的方法
为了提升用户在大型复杂建筑中到达目的地的体验,我们利用 Unity 和智能手机开发了一个名为 INSUS 的室内导航系统。它可以使用快速反应(QR)代码重置用户位置,以减少用户在导航过程中迷失方向的情况。然而,这种方法需要在现场准备大量 QR 码纸,在实施时造成额外负担。在本文中,我们提出了另一种重置方法,即利用对象检测和光学字符识别(OCR)技术识别现场自然安装的标识信息,从而减少负载。建筑物中存在大量标识,其中包含房间号、房间名和楼层号等文本。在该提案中,标志图像由智能手机拍摄,标志由 YOLOv8 检测,标志内的文字由 PaddleOCR 识别,并使用列文森距离与房间数据库中的每条记录进行比较。为了进行评估,我们在日本冈山大学的两栋建筑中应用了该建议。结果表明,YOLOv8 的 mAP@0.5 达到 0.995,mAP@0.5:0.95 达到 0.978,而 PaddleOCR 可以准确提取标志图像中的文本,平均 CER% 低于 10%。与之前的方法相比,YOLOv8 和 PaddleOCR 的组合减少了 6.71s 的执行时间。结果证实了该建议的有效性。
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