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Leveraging retinanet based object detection model for assisting visually impaired individuals with metaheuristic

Alaa O Khadidos1, Ayman Yafoz2,3

  • 1Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

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|May 9, 2025
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Summary
This summary is machine-generated.

This study introduces a new object detection model (ODMVII-MOA) to help visually impaired individuals navigate daily tasks. The model achieves 99.69% accuracy in recognizing objects, significantly improving assistive technology for the visually impaired.

Keywords:
Computer visionDandelion optimizerDeep learningFeature extractionObject detectionVisually impaired individuals

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Assistive Technology

Background:

  • Visually impaired individuals face significant challenges in everyday activities.
  • Existing object detection methods require improvement for practical application in assistive technologies.
  • Deep learning-based object recognition offers autonomous feature extraction for enhanced detection.

Purpose of the Study:

  • To propose a novel Object Detection Model for Visually Impaired Individuals with a Metaheuristic Optimization Algorithm (ODMVII-MOA).
  • To enhance real-time object detection and recognition capabilities for visually impaired individuals.
  • To improve the accuracy and reliability of object detection in challenging conditions like low light and occlusion.

Main Methods:

  • Image pre-processing using the Weiner filter (WF) for noise reduction.
  • Object detection utilizing the RetinaNet technique.
  • Feature extraction with EfficientNetB0 and classification using LSTM-Autoencoder (LSTM-AE).
  • Hyperparameter optimization of LSTM-AE via the Dandelion Optimizer (DO).

Main Results:

  • The ODMVII-MOA model demonstrated superior performance in object detection tasks.
  • Experimental validation on an indoor dataset showed high accuracy.
  • Achieved a classification accuracy of 99.69%, outperforming existing methods.

Conclusions:

  • The proposed ODMVII-MOA technique significantly enhances object detection for the visually impaired.
  • The integration of advanced deep learning and metaheuristic optimization yields state-of-the-art results.
  • This model holds promise for developing more effective navigation and object recognition tools for visually impaired individuals.