Citation

Sonai Muthu Anbananthen, Kalaiarasi and Subbiah, Sridevi and Baskar, Subiksha Gayathri and Selvaraj, Ratchana and Krishnan, Jayakumar and Kannan, Subarmaniam and Chelliah, Deisy (2023) The Eye: A Light Weight Mobile Application for Visually Challenged People Using Improved YOLOv5l Algorithm. Emerging Science Journal, 7 (5). pp. 1636-1652. ISSN 2610-9182

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Text to Speech; Visually Challenged; YOLO; Machine Learning.
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	07 Dec 2023 03:30
Last Modified:	07 Dec 2023 03:30
URII:	http://shdl.mmu.edu.my/id/eprint/11941

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