Abstract: One of the biggest challenges facing blind assistance systems is how they can navigate with safety and independence in such complicated real-world scenarios, given that traditional tools that assist these users are usually simplistic. Among many techniques that emerge as essential to upgrading these systems are machine learning and deep learning. These methods introduce considerable object detection, voice recognition, and distance measurement capabilities. This review summarizes the findings of recent studies in the application of neural networks, such as convolutional neural networks (CNNs), and advanced models in real-time object recognition and environmental awareness. Models like Faster R-CNN, SSD, and DenseNet have shown exceptional performance in object detection and segmentation with high accuracy rates and reliability. However, the challenges include diversity in datasets, limitations in real-time processing, and user adaptability. Furthermore, computational efficiency and optimizing deep learning models for low-power devices remain crucial areas for improvement. Enhancing multimodal feedback, integrating adaptive learning models, and improving response time are essential for real-world deployment. This review represents a great step forward in assistive technology, providing real-time, reliable feedback to help visually impaired users navigate their surroundings with greater independence and confidence.
Keywords: Visually Impaired, Computer Vision, Deep Learning, Object Detection, YOLO Algorithm, Real time.
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DOI:
10.17148/IJARCCE.2025.14673
