Smart Wearable Vision-Assistance System for Visually Impaired Using Edge-Based Deep Learning

A Multi-Modal Deep Learning Framework for Assistive Vision: Software-Based Design and Real-Time Validation

The development of an intelligent visual aid incorporating object detection, face recognition, distance estimation, distance estimation, and text-to-speech audio feedback is outlined.

AI-Based Object Detection and Smart Navigation System for the Visually Impaired

An AI-powered assistive system designed to enhance the mobility, safety, and independence of visually impaired individuals, creating a smart, voice-guided companion that empowers visually impaired users to navigate their surroundings with confidence and independence.

Smart Sight: A Comprehensive Deep Learning Framework for Real-Time Assistive Navigation and Object Recognition for Visually Impaired Individuals

Visual impairment is a universal health burden, and one of the most pressing concerns in the world, as it is estimated that there are 2.2 billion afflicted persons in the world, which is highly constraining in their free movements, their interactions with the surrounding, as well as on their social interrelation. White canes and guide dogs, which are some of the classical assistive solutions, may only offer the simplest contextual awareness and lack the semantic awareness of the scene to crawl safely and reliably through the complex real-world world. In this paper, I will introduce Smart Sight, a custom real-time assistive navigation architecture that tightly integrates four existing state-of-theart deep learning models, such as YOLOv8nano to identify objects, Deep-SORT to track multiple objects, MiDaS v3.0 to estimate object and face depth, and a priority-based Text-to-Speech (TTS) engine to offer intelligent feedback, and none The proposed system achieves an average of 94.3% of the Mean Average Precision (mAP) on the MS COCO data benchmark and 90.9% on a non-test dataset of three classes in the context of road-anomaly and can continue to provide end-to-end inference of 22 frames per second with an aggregate latency of 145 milliseconds on consumer mobile hardware.

Det2Pose: Lightweight Visual Sensing and Computation Framework for 3D Perception in Consumer Autonomous Intelligent Systems

Context-Aware Real-Time Assistive Navigation using Deep Learning and Voice Feedback for Complex Environments

An Assistive Object Recognition System for Visually Impaired Individuals Using Mask R-CNN

Blind and partially sighted individuals often face significant challenges in identifying and interacting with objects in their surroundings, which limits their independence and daily functioning. To address this issue, this study proposes a deep learning-based assistive framework built on mask region-based convolutional neural network (Mask R-CNN) for real-time object detection and recognition in both indoor and outdoor environments. The proposed system integrates sensor-based obstacle awareness with vision-based object analysis to enhance environmental perception, enabling robust identification of surrounding objects at varying distances and under varying environmental conditions. In this study, object detection refers to the localization and identification of objects in the scene, whereas object recognition refers to the classification of detected objects into predefined semantic categories. The object detection performance is evaluated across multiple object-to-system distances, achieving a maximum accuracy of 99.88% at 100 cm in indoor environments and 99.64% at 120 cm in outdoor environments. Further, object recognition is assessed across five representative categories, namely person, chair, table, stairs, and washroom. The proposed framework achieves average recognition accuracies of 97.29% and 98.53% in indoor and outdoor environments, respectively, yielding an overall average of 97.91%. In addition, the optimized implementation achieves an inference speed of 10-12 frames per second (FPS), corresponding to a processing time of 0.0833-0.1000 s per frame, demonstrating suitability for real-time assistive applications. Experimental results demonstrate that the proposed framework maintains consistent performance across diverse operating conditions, highlighting its robustness for real-world deployment. Consequently, the system offers significant potential to enhance the autonomy, safety, and quality of life of visually impaired individuals.