Abstract :

To Improve Accessibility And Encourage Inclusivity In A Variety Of Contexts, A Deep Learningbased Method For Identifying Individuals With Disabilities Is Essential. Several YOLO Models (v5s, V7-tiny, V8, V5x6, And V9) And Sophisticated Object Detection Algorithms, Such As FastRCNN And FasterRCNN, Are Used To Create A Strong Foundation For Precise Identification And Recognition. With An Emphasis On Accuracy And Real-time Speed, These Models Make Use Of Cutting-edge Architectures To Enhance Detection Capabilities. The Most Recent Iterations Of YOLO Combined With FasterRCNN Allow For Thorough Analysis And Detection, Supporting A Variety Of Circumstances And Guaranteeing Accurate Results. The YOLO Family Of Models Is Especially Good At Processing Images Quickly Without Sacrificing Accuracy, Which Makes It Appropriate For Use In Dynamic Settings. The Flask Framework Will Be Used To Create An Intuitive Front End With Secure Access Capabilities For Authentication. Through Improved Resource Allocation And Well-informed Decisionmaking In Accessibility Projects, This Approach Seeks To Support And Monitor People With Disabilities, Ultimately Fostering A More Inclusive Society. Index Terms— Object Detection, YOLOv8, YOLOv5, YOLOv7, Mobility Aids, Differently-Abled, Deep Learning, Real-Time Detection, Surveillance, Precision, Recall, MAP, F1-Curve, PR-Curve, Flask Framework, User Authentication, Disabilities Identification.

Published:

07-11-2025

Issue:

Vol. 25 No. 11 (2025)

Page Nos:

63-72

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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