Towards Improved Assistive Technologies: Classification and Evaluation of Object Detection Techniques for Users with Visual Impairments

Even though millions of people struggle to interact with the outside world due to visual impairments, vision is an essential part of our daily lives. Because of its ability to identify and navigate around objects in their surroundings, object detection a crucial component of computer vision has become a potentially helpful solution. This study offers a thorough analysis of object detection techniques utilizing a dual classification system that combines traditional and deep learning methods. In addition, we analyze the most popular evaluation metrics and datasets for these systems' training and evaluation. Unlike previous surveys, our work provides a unique perspective by carefully examining the latest advancements in both innovative deep learning models and traditional approaches. The survey's conclusion highlights current problems and recommends future research directions, highlighting the need for more effective models, diverse datasets, and multi-modal data integration to improve assistive technologies for the visually impaired.