International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 01 | Jan 2024
p-ISSN: 2395-0072
www.irjet.net
Object Detection and Localization for Visually Impaired People using CNN Sony Tirkey1, Anmol Ratan Tirkey2, Cazal Tirkey3 1Student, CHRIST (Deemed-To-Be University), Bengaluru, Karnataka, India 2 Student, JAIN (Deemed-To-Be University), Bengaluru, Karnataka, India
3Researcher, Bengaluru, Karnataka, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Visually impaired people constitute a significant
The image processing vision module described in this system is an integrated aspect of the platform dedicated to assist visually impaired people. Furthermore, the provided module can be used independently of the integrated platform. The proposed vision-based guidance system is created, built, and tested throughout experiments and iteratively optimized. The module follows the principle of producing a highperformance device that is also cost-effective for practical use. The module employs disruptive technology and permits updates and the addition of new functionality.
portion of the global population, with both permanent and temporary disabilities. WHO estimates around 390 lakh individuals are completely blind, and 2850 lakh individuals are purblind or visually impaired. To help them aid in daily navigation, numerous supporting systems are being developed which had numerous disadvantages. Our main objective is to create an auto-assistance system for the visually impaired. By using CNNs (Convolution Neural Network), a widely-used approach in deep learning models, our system achieves over 95% accuracy in object detection based on camera images. Identified objects are conveyed through voice messages, making it a valuable prototype for assisting the visually impaired.
WORK DONE Downloaded the project's model file.
2. EXISTING SYSTEMS
Key Words: Visually Impaired, Object Detection, CNN, Deep Learning, assistance.
Convolutional Neural Networks (CNN), speech recognition, smartphone camera, and object personalization were all used in existing systems. The purpose is to help visually impaired people navigate indoor surroundings, recognize items, and avoid obstacles.
1. INTRODUCTION Visually impaired people constitute a significant portion of the population component, with tens of millions predicted to exist globally. Their integration into society is an essential and ongoing aim. A lot of work has gone into ensuring a health-care system, to help visually impaired people live a normal life, many guiding system approaches have been created. These systems are frequently created just for certain activities. However, these solutions can significantly improve such people's ability to move and their security.
By using facial recognition for authentication, the Facial Identification and Authentication System provides a secure and personalized user experience while also ensuring that only authorized users may access the system and its features. Nonetheless, it is dependent on the accuracy of facial recognition technology, which can be influenced by lighting, changes in look, and other factors.
The advancement of cutting-edge guiding systems to assist visually impaired persons is tightly linked to advanced technologies in image processing and computer vision, as well as the speed of the devices and unit processors. Regardless of the technology used, the application must work in real time with quick actions and decisions, as speed is crucial for taking action.
The Object Detection System (General Object Detection Model 1) employs a pre-trained CNN model for general object detection, allowing the system to identify a wide range of items and providing real-time object recognition, which improves the user's comprehension of their surroundings. However, it is restricted to the items and categories in the pre-trained model. Objects that are not part of the model's training data may not be detected accurately. The Customized Object Detection System (Model 2) allows users to personalize the system by adding their own detection objects, increasing the system's versatility and usability for visually impaired users with special needs. However, users must take and label photographs, which can be time-consuming. Accuracy may also vary depending on the quality of photographs captured by users. Distance
Choosing the best possible outcome is essentially a trade-off between the performance of the software component and the hardware capabilities. It is necessary to adjust the parameters to optimum. One of the primary goals of the aided system during a visually impaired person's indoor movement is to automatically identify and recognize objects or obstacles, followed by an auditory alert.
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