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– ABSTRACT
Introduction: Object detection systems have significantly advanced, yet they often lack accessibility features for visually impaired individuals. This paper introduces a novel approach, combining object detection with voice feedback, to enhance accessibility and usability for this demographic.
Methodology: Utilizing deep learning models, particularly convolutional neural networks (CNNs), objects are detected in real-time from images or video frames. Upon detection, a voice feedback system promptly announces the identified objects, enabling users to interact with their environment more effectively.
Implementation: In implementing the system, we utilized a combination of the Python programming language, the OpenCV library for image processing, and speech synthesis modules for generating voice feedback. We trained the CNN model on a diverse dataset to ensure robust detection across various object categories.
Evaluation: To assess the system’s performance, we conduct extensive testing with visually impaired individuals in real-world scenarios, measuring metrics such as detection accuracy, speed, and user satisfaction to validate the efficacy of the proposed approach.
Results: The results demonstrate the effectiveness of the object detection with voice feedback system in aiding visually impaired users to navigate their surroundings independently. High detection accuracy coupled with real-time voice feedback significantly improves user experience and fosters greater autonomy.
Discussion: This innovative approach not only addresses accessibility challenges but also opens avenues for integrating voice-based interfaces into object detection systems. The seamless interaction between visual and auditory modalities enhances the overall usability of the system.
Conclusion: Object detection with voice feedback presents a promising solution for enhancing accessibility in assistive technologies.
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