Obstacle Detection For Visually Impaired

Abstract

The paper examines innovative obstacle-detection solutions designed for visually impaired people who do not rely on traditional sensors and recognize the challenges they face when navigating their environment independently. It delves into techniques leveraging alternative sensory modalities such as auditory and tactile feedback, prioritizing user experience and safety. Evaluation criteria encompass effectiveness, ease of use, and affordability, during discussions involve adapting existing infrastructure for tactile navigation and collaboration with guide animal organizations to devise comprehensive solutions. In addition to case studies showing the successful implementation of sensor-free obstacle detection in a wide range of environments, i.e., cities and indoors, challenges related to training and social acceptance of alternative methods will be addressed. In particular, the paper offers beneficial insight into novel approaches to detecting obstacles aimed at improving the mobility and independence of those with visual impairment. Our method uses advanced machine learning algorithms to interpret and predict potential obstacles in real-time. Our system can identify and classify barriers with a high degree of accuracy through analysis of user's sound feedback from their surroundings as well as the use of sophisticated learning techniques. In a wide range of real-world scenarios, we demonstrate the effectiveness of our approach through extensive testing with visually impaired participants. The results show that compared to conventional sensor-based methods, the performance of obstacle detection is significantly improved. In addition, our solution is designed to improve the flexibility, affordability, and ease of integration with existing technologies to create an environment more conducive for people with visual impairment.