Open Access

ARTICLE

Automated Identification Algorithm Using CNN for Computer Vision in Smart Refrigerators

Pulkit Jain¹, Paras Chawla¹, Mehedi Masud^2,*, Shubham Mahajan³, Amit Kant Pandit³

1 Chandigarh University, Mohali, 140413, India
2 Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
3 School of Electronics & Communication Engineering, Shri Mata Vaishno Devi University, Katra, 182320, India

* Corresponding Author: Mehedi Masud. Email:

(This article belongs to this Special Issue: Applications of Intelligent Systems in Computer Vision)

Computers, Materials & Continua 2022, 71(2), 3337-3353. https://doi.org/10.32604/cmc.2022.023053

Received 26 August 2021; Accepted 18 October 2021; Issue published 07 December 2021

Abstract

Machine Learning has evolved with a variety of algorithms to enable state-of-the-art computer vision applications. In particular the need for automating the process of real-time food item identification, there is a huge surge of research so as to make smarter refrigerators. According to a survey by the Food and Agriculture Organization of the United Nations (FAO), it has been found that 1.3 billion tons of food is wasted by consumers around the world due to either food spoilage or expiry and a large amount of food is wasted from homes and restaurants itself. Smart refrigerators have been very successful in playing a pivotal role in mitigating this problem of food wastage. But a major issue is the high cost of available smart refrigerators and the lack of accurate design algorithms which can help achieve computer vision in any ordinary refrigerator. To address these issues, this work proposes an automated identification algorithm for computer vision in smart refrigerators using InceptionV3 and MobileNet Convolutional Neural Network (CNN) architectures. The designed module and algorithm have been elaborated in detail and are considerably evaluated for its accuracy using test images on standard fruits and vegetable datasets. A total of eight test cases are considered with accuracy and training time as the performance metric. In the end, real-time testing results are also presented which validates the system's performance.

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Keywords

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