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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
Article Submitted : 21604
Article Published : 8531
Total Authors : 22438
Total Reviewer : 805
Total Countries : 159
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Published Paper Details
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| Paper Title: | Object Detection For Visually Impaired People Using Yolo V4 Algorithm |
| Authors Name: | Nivetha.M , Rohini.A , Monika.N , Ashika.K |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_184021
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| Published Paper Id: | IJRTI2209039 |
| Published In: | Volume 7 Issue 9, September-2022 |
| DOI: | |
| Abstract: | ABSTRACT Blind navigation has become a challenging task nowadays. Blind people cannot detect and avoid obstacles like sighted people and need guidance to avoid such obstacles. But the limited potential of white canes makes it impossible for a navigator to detect all possible threats. Therefore, there is not enough aid to navigate safely on the white cane. To protect blind people's safe and independent navigation, more insight into their current environment must be provided. This study proposes a novel approach for obstacle detection based on deep learning to assist in blind navigation.In this study, a prototype was developed using single-shot detector (SSD) for obstacle detection and distance estimation due to real-time performance and high accuracy of SSDs. To train the SSD for obstacle detection data was gathered using a simulation environment. The result of the obstacle detection model was used to estimate the distance of the obstacles. The final result is communicated to the user through audio sequences by combining the feedback from obstacle detection and distance estimation.. The prototype system is deployed on a smartphone and a real-time video stream captured by the smartphone camera is processed to detect obstacles. To train the SSD for obstacle detection SSD MobileNet Architecture was used and the data to train the SSD was generated using a simulation environment. To estimate the distance of the detected obstacles, SSD based MonoDepth algorithm was used.The mean average precision (mAP) value of all the classes of the SSD for obstacle detection reached more than 70%. High accuracy and high speed of obstacle detection can be achieved by computer simulation but there is delay when estimating distance. Usability and efficiency of the prototype system exceeded 65% according to the usability evaluation feedback. |
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| Cite Article: | "Object Detection For Visually Impaired People Using Yolo V4 Algorithm", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 9, page no.302 - 308, September-2022, Available :http://www.ijrti.org/papers/IJRTI2209039.pdf |
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| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2209039
Registration ID:184021
Published In: Volume 7 Issue 9, September-2022
DOI (Digital Object Identifier):
Page No: 302 - 308 Country: Thoothukudi, Tamilnadu, India Research Area: Information Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2209039 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2209039 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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