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A smart assistive system for visually challenged people through efficient object detection using deep learning with

Abdullah M Alashjaee1, Asma A Alhashmi1, Abdulbasit A Darem2,3

  • 1Department of Computer Science, College of Science, Northern Border University, Arar, Saudi Arabia.

Scientific Reports
|November 27, 2025
PubMed
Summary
This summary is machine-generated.

A novel Smart Assistive System for the Visually Challenged (SASVCP-ODTSA) uses object detection to improve daily task completion. This system achieves 99.58% accuracy, significantly aiding visually impaired individuals.

Keywords:
CapsNetObject detectionSmart assistive systemTunicate swarm algorithmVisually challenged people

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Assistive Technology

Background:

  • Visual impairment presents significant challenges in performing everyday tasks.
  • Accurate object detection is crucial for developing effective assistive technologies for the visually impaired.
  • Current methods require enhancement for reliable object recognition in diverse environments.

Purpose of the Study:

  • To propose a Smart Assistive System for the Visually Challenged People through Object Detection Using the Tunicate Swarm Algorithm (SASVCP-ODTSA).
  • To enhance object detection accuracy and classification performance for assisting visually impaired individuals.
  • To automatically detect and classify objects in images to aid daily activities.

Main Methods:

  • Image pre-processing using Median Filtering (MF) for noise reduction.
  • Object detection utilizing the YOLOV8 method.
  • Feature extraction with the CapsNet model.
  • Object detection and classification using a Deep Belief Network (DBN) optimized by the Tunicate Swarm Algorithm (TSA).

Main Results:

  • The SASVCP-ODTSA model achieved a superior accuracy of 99.58% on the Indoor Object Detection dataset.
  • The Tunicate Swarm Algorithm effectively optimized the Deep Belief Network parameters for improved classification.
  • The proposed system demonstrated robust object detection and classification capabilities.

Conclusions:

  • The SASVCP-ODTSA system offers a significant advancement in assistive technology for the visually impaired.
  • High accuracy object detection and classification are vital for enhancing independence and task completion for visually challenged individuals.
  • The integration of MF, YOLOV8, CapsNet, and TSA-optimized DBN presents a powerful approach for object detection applications.