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Related Experiment Video

Updated: Sep 11, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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An intelligent object detection and classification framework for assisting visually challenged persons using deep

Alaa O Khadidos1, Ayman Yafoz2,3

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

Scientific Reports
|August 15, 2025
PubMed
Summary
This summary is machine-generated.

A new Hybrid Deep Learning Model for Object Detection and Classification Using an Improved Crow Search Algorithm (HDLMODC-ICSA) accurately identifies objects for visually impaired individuals. This advanced assistive technology achieves 99.59% accuracy in real-time indoor environments.

Keywords:
Hybrid deep learning modelsImage pre-processingImproved crow search algorithmObject detectionVisually challenged person

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Assistive Technology

Background:

  • One billion people live with disabilities, driving the need for assistive technologies.
  • Object detection and classification are key computer vision technologies for identifying objects in images and videos.
  • Deep learning models show promise in improving object detection accuracy, particularly for real-time applications.

Purpose of the Study:

  • To propose a Hybrid Deep Learning Model for Object Detection and Classification Using an Improved Crow Search Algorithm (HDLMODC-ICSA).
  • To develop an accurate and real-time object recognition system to aid visually impaired individuals.
  • To enhance independence and accessibility for people with disabilities through advanced technology.

Main Methods:

  • Image pre-processing using median filtering (MF) to enhance image clarity.
  • Object detection using the Faster R-CNN model for efficient region proposal and detection.
  • Feature extraction with the Improved LeNet-5 model and classification using an attention-based stacked bi-directional long short-term memory (ABS-Bi-LSTM) network.
  • Hyperparameter optimization of the ABS-Bi-LSTM model via the improved crow search algorithm (ICSA).

Main Results:

  • The HDLMODC-ICSA method achieved a superior accuracy of 99.59% in object detection and classification.
  • The model demonstrated effective real-time object recognition in indoor environments.
  • Comprehensive studies validated the approach's efficiency using the Indoor Objects Detection dataset.

Conclusions:

  • The proposed HDLMODC-ICSA method significantly advances object detection and classification for assistive technology.
  • This technique offers a highly accurate and efficient solution for real-time object recognition, particularly benefiting visually impaired users.
  • The study highlights the potential of hybrid deep learning models and optimization algorithms in creating impactful assistive solutions.