Evianita Dewi Fajrianti, Y. Panduman, Nobuo Funabiki, Amma Liesvarastranta Haz, Komang Candra Brata, S. Sukaridhoto
{"title":"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","authors":"Evianita Dewi Fajrianti, Y. Panduman, Nobuo Funabiki, Amma Liesvarastranta Haz, Komang Candra Brata, S. Sukaridhoto","doi":"10.3390/network4030014","DOIUrl":null,"url":null,"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.","PeriodicalId":19145,"journal":{"name":"Network","volume":"31 13","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A User Location Reset Method through Object Recognition in Indoor Navigation System Using Unity and a Smartphone (INSUS)\",\"authors\":\"Evianita Dewi Fajrianti, Y. Panduman, Nobuo Funabiki, Amma Liesvarastranta Haz, Komang Candra Brata, S. Sukaridhoto\",\"doi\":\"10.3390/network4030014\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":19145,\"journal\":{\"name\":\"Network\",\"volume\":\"31 13\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Network\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/network4030014\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Network","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/network4030014","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A User Location Reset Method through Object Recognition in Indoor Navigation System Using Unity and a Smartphone (INSUS)
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.