Open Access

ARTICLE

Intelligent Deep Convolutional Neural Network Based Object Detection Model for Visually Challenged People

by S. Kiruthika Devi¹, Amani Abdulrahman Albraikan², Fahd N. Al-Wesabi³, Mohamed K. Nour⁴, Ahmed Ashour⁵, Anwer Mustafa Hilal^6,*

1 Department of Computing Technology, SRM Institute of Science and Technology, India
2 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia
3 Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Saudi Arabia
4 Department of Computer Science, College of Computing and Information System, Umm Al-Qura University, Saudi Arabia
5 Department of Engineering Mathematics and Physics, Faculty of Engineering and Technology, Future University in Egypt, New Cairo, 11845, Egypt
6 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

* Corresponding Author: Anwer Mustafa Hilal. Email:

Computer Systems Science and Engineering 2023, 46(3), 3191-3207. https://doi.org/10.32604/csse.2023.036980

Received 18 October 2022; Accepted 21 December 2022; Issue published 03 April 2023

Abstract

Artificial Intelligence (AI) and Computer Vision (CV) advancements have led to many useful methodologies in recent years, particularly to help visually-challenged people. Object detection includes a variety of challenges, for example, handling multiple class images, images that get augmented when captured by a camera and so on. The test images include all these variants as well. These detection models alert them about their surroundings when they want to walk independently. This study compares four CNN-based pre-trained models: Residual Network (ResNet-50), Inception v3, Dense Convolutional Network (DenseNet-121), and SqueezeNet, predominantly used in image recognition applications. Based on the analysis performed on these test images, the study infers that Inception V3 outperformed other pre-trained models in terms of accuracy and speed. To further improve the performance of the Inception v3 model, the thermal exchange optimization (TEO) algorithm is applied to tune the hyperparameters (number of epochs, batch size, and learning rate) showing the novelty of the work. Better accuracy was achieved owing to the inclusion of an auxiliary classifier as a regularizer, hyperparameter optimizer, and factorization approach. Additionally, Inception V3 can handle images of different sizes. This makes Inception V3 the optimum model for assisting visually challenged people in real-world communication when integrated with Internet of Things (IoT)-based devices.

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Keywords

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