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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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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
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.