Assistive Technologies That Utilize Object Detection Have The Potential To Significantly Improve The Independence And Safety Of Visually Impaired Individuals. Object Detection Is The Process Of Identifying And Localizing Objects Within An Image Or Video Sequence, Typically Using Deep Learning Algorithms Trained On Annotated Datasets. For The Blind, Object Detection Systems Can Be Used To Provide Real-time Feedback About The Presence And Location Of Objects In The Environment, Using Sensors Or Cameras Mounted On A Wearable Device. This Technology Has Been Implemented In Various Systems, Such As The Seeing AI App Developed By Microsoft, The Horus System Developed By The University Of Michigan, And The NAVIS System Developed By The University Of California. Ongoing Research In This Field Is Focused On Improving The Accuracy And Speed Of Object Detection Algorithms And Developing New Wearable Devices And Sensor Technologies To Make Object Detection Systems More Practical And Accessible For Everyday Use. To Ensure Real-time Performance, The System Is Optimized For Deployment On Edge Devices With Limited Computational Resources. The Architecture Is Designed To Balance Accuracy And Efficiency, Considering The Constraints Imposed By Mobile And Wearable Devices Commonly Used By Visually Impaired Individuals. Additionally, The Proposed Solution Aims To Be Robust To Varying Environmental Conditions, Including Changes In Lighting, Occlusions And Object Orientations. In This Report Paper, We Provide An Overview Of The Current State Of Object Detection For The Blind And Highlight The Potential Impact Of This Technology In Improving The Quality Of Life For Visually Impaired Individuals.
