Abstract
This survey addresses visual impairment by leveraging advanced technology to empower independent navigation for visually impaired individuals. It emphasizes the importance of tailored systems and highlights successful studies implementing innovative solutions. Incorporating diverse technologies like object detection, NLP, and information retrieval, the project explores deep learning algorithms such as CNN, RNN, and YOLO, alongside audio-based input/output integration. It recommends utilizing APIs like Google Text-to-Speech and Python libraries for efficient implementation, aiming to enhance system functionality and accessibility. Ultimately, the survey aims to aid in selecting appropriate tools and methodologies for developing audio-based object detection systems, contributing to ongoing efforts in supporting visually impaired individuals. Key Words: Object Detection, Natural Language Processing, Audio Data, YOLO, Real-Time identification, Visually Impaired, Image Captioning.
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