The Eye: A Light Weight Mobile Application for Visually Challenged People Using Improved YOLOv5l Algorithm
The eye is an essential sensory organ that allows us to perceive our surroundings at a glance. Losing this sense can result in numerous challenges in daily life. However, society is designed for the majority, which can create even more difficulties for visually impaired individuals. Therefore, empow...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Ital Publication
2023-10-01
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| Series: | Emerging Science Journal |
| Subjects: | |
| Online Access: | https://www.ijournalse.org/index.php/ESJ/article/view/1877 |
| _version_ | 1797356336988553216 |
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| author | Kalaiarasi Sonai Muthu Anbananthen Sridevi Subbiah Subiksha Gayathri Baskar Ratchana Selvaraj Jayakumar Krishnan Subarmaniam Kannan Deisy Chelliah |
| author_facet | Kalaiarasi Sonai Muthu Anbananthen Sridevi Subbiah Subiksha Gayathri Baskar Ratchana Selvaraj Jayakumar Krishnan Subarmaniam Kannan Deisy Chelliah |
| author_sort | Kalaiarasi Sonai Muthu Anbananthen |
| collection | DOAJ |
| description | The eye is an essential sensory organ that allows us to perceive our surroundings at a glance. Losing this sense can result in numerous challenges in daily life. However, society is designed for the majority, which can create even more difficulties for visually impaired individuals. Therefore, empowering them and promoting self-reliance are crucial. To address this need, we propose a new Android application called “The Eye” that utilizes Machine Learning (ML)-based object detection techniques to recognize objects in real-time using a smartphone camera or a camera attached to a stick. The article proposed an improved YOLOv5l algorithm to improve object detection in visual applications. YOLOv5l has a larger model size and captures more complex features and details, leading to enhanced object detection accuracy compared to smaller variants like YOLOv5s and YOLOv5m. The primary enhancement in the improved YOLOv5l algorithm is integrating L1 and L2 regularization techniques. These techniques prevent overfitting and improve generalization by adding a regularization term to the loss function during training. Our approach combines image processing and text-to-speech conversion modules to produce reliable results. The Android text-to-speech module is then used to convert the object recognition results into an audio output. According to the experimental results, the improved YOLOv5l has higher detection accuracy than the original YOLOv5 and can detect small, multiple, and overlapped targets with higher accuracy. This study contributes to the advancement of technology to help visually impaired individuals become more self-sufficient and confident.
Doi: 10.28991/ESJ-2023-07-05-011
Full Text: PDF |
| first_indexed | 2024-03-08T14:25:09Z |
| format | Article |
| id | doaj.art-632ba94575844b39b4f84a6b10c49d2e |
| institution | Directory Open Access Journal |
| issn | 2610-9182 |
| language | English |
| last_indexed | 2024-03-08T14:25:09Z |
| publishDate | 2023-10-01 |
| publisher | Ital Publication |
| record_format | Article |
| series | Emerging Science Journal |
| spelling | doaj.art-632ba94575844b39b4f84a6b10c49d2e2024-01-13T07:27:37ZengItal PublicationEmerging Science Journal2610-91822023-10-01751636165210.28991/ESJ-2023-07-05-011547The Eye: A Light Weight Mobile Application for Visually Challenged People Using Improved YOLOv5l AlgorithmKalaiarasi Sonai Muthu Anbananthen0Sridevi Subbiah1Subiksha Gayathri Baskar2Ratchana Selvaraj3Jayakumar Krishnan4Subarmaniam Kannan5Deisy Chelliah6Faculty of Information Science and Technology, Multimedia University, Selangor,Thiagarajar College of Engineering, Madurai, Tamilnadu,Thiagarajar College of Engineering, Madurai, Tamilnadu,Thiagarajar College of Engineering, Madurai, Tamilnadu,Faculty of Information Science and Technology, Multimedia University, Selangor,Faculty of Information Science and Technology, Multimedia University, Selangor,Thiagarajar College of Engineering, Madurai, Tamilnadu,The eye is an essential sensory organ that allows us to perceive our surroundings at a glance. Losing this sense can result in numerous challenges in daily life. However, society is designed for the majority, which can create even more difficulties for visually impaired individuals. Therefore, empowering them and promoting self-reliance are crucial. To address this need, we propose a new Android application called “The Eye” that utilizes Machine Learning (ML)-based object detection techniques to recognize objects in real-time using a smartphone camera or a camera attached to a stick. The article proposed an improved YOLOv5l algorithm to improve object detection in visual applications. YOLOv5l has a larger model size and captures more complex features and details, leading to enhanced object detection accuracy compared to smaller variants like YOLOv5s and YOLOv5m. The primary enhancement in the improved YOLOv5l algorithm is integrating L1 and L2 regularization techniques. These techniques prevent overfitting and improve generalization by adding a regularization term to the loss function during training. Our approach combines image processing and text-to-speech conversion modules to produce reliable results. The Android text-to-speech module is then used to convert the object recognition results into an audio output. According to the experimental results, the improved YOLOv5l has higher detection accuracy than the original YOLOv5 and can detect small, multiple, and overlapped targets with higher accuracy. This study contributes to the advancement of technology to help visually impaired individuals become more self-sufficient and confident. Doi: 10.28991/ESJ-2023-07-05-011 Full Text: PDFhttps://www.ijournalse.org/index.php/ESJ/article/view/1877text to speechvisually challengedyolomachine learning. |
| spellingShingle | Kalaiarasi Sonai Muthu Anbananthen Sridevi Subbiah Subiksha Gayathri Baskar Ratchana Selvaraj Jayakumar Krishnan Subarmaniam Kannan Deisy Chelliah The Eye: A Light Weight Mobile Application for Visually Challenged People Using Improved YOLOv5l Algorithm Emerging Science Journal text to speech visually challenged yolo machine learning. |
| title | The Eye: A Light Weight Mobile Application for Visually Challenged People Using Improved YOLOv5l Algorithm |
| title_full | The Eye: A Light Weight Mobile Application for Visually Challenged People Using Improved YOLOv5l Algorithm |
| title_fullStr | The Eye: A Light Weight Mobile Application for Visually Challenged People Using Improved YOLOv5l Algorithm |
| title_full_unstemmed | The Eye: A Light Weight Mobile Application for Visually Challenged People Using Improved YOLOv5l Algorithm |
| title_short | The Eye: A Light Weight Mobile Application for Visually Challenged People Using Improved YOLOv5l Algorithm |
| title_sort | eye a light weight mobile application for visually challenged people using improved yolov5l algorithm |
| topic | text to speech visually challenged yolo machine learning. |
| url | https://www.ijournalse.org/index.php/ESJ/article/view/1877 |
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