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  1. Home
  3. Enhanced Pedestrian Walkway Object Detection Using Deep Learning And Pelican Optimization Algorithm For Assisting Disabled Persons.
  1. Home
  3. Enhanced Pedestrian Walkway Object Detection Using Deep Learning And Pelican Optimization Algorithm For Assisting Disabled Persons.

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Enhanced pedestrian walkway object detection using deep learning and pelican optimization algorithm for assisting

Fadwa Alrowais1, Mona Almofarreh2, Radwa Marzouk3,4

  • 1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.

Scientific Reports
|December 10, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces an Enhanced Pedestrian Walkway Object Detection and Pelican Optimization Algorithm for Assisting Disabled Persons (EPWOD-POAADP) to improve navigation for visually impaired individuals. The method enhances object detection and classification for safer pedestrian walkways.

Keywords:
Disabled personsFaster R-CNNObject detectionPedestrian walkwayPelican optimization algorithm

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Assistive Technology

Background:

  • Pedestrian navigation for individuals with blindness presents significant challenges.
  • Existing assistive devices like white canes and GPS have limitations in environmental perception.
  • Deep learning and machine learning show promise for vision navigation tasks.

Purpose of the Study:

  • To propose an Enhanced Pedestrian Walkway Object Detection and Pelican Optimization Algorithm for Assisting Disabled Persons (EPWOD-POAADP).
  • To enhance pedestrian walkways for improved navigation for blind individuals.

Main Methods:

  • Image pre-processing using median filtering (MF).
  • Object detection with Faster R-CNN and feature extraction with CapsNet.
  • Detection and classification using wavelet neural networks (WNN).
  • Hyperparameter optimization of WNN via Pelican Optimization Algorithm (POA).

    • Main Results:

    • The EPWOD-POAADP method demonstrated enhanced performance in experimental evaluations.
    • The approach effectively identifies and classifies objects crucial for navigation.
    • The system was validated using the UCSD anomaly detection dataset.

    Conclusions:

    • The proposed EPWOD-POAADP method offers a significant advancement in assistive technology for the visually impaired.
    • This approach improves the safety and independence of blind pedestrians.
    • Further research can build upon these findings to refine navigation systems.