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.
Highlights
1 Introduction ‘Smart home’ is not a new concept as Internet of Things (IoT) is playing a great role in revolutionizing the way one ever thought of living in a home filled with sensors where every electronic appliance can talk to one another wirelessly
No research article mentions about placing the weight measurement system and cameras outside the fridge as it can help avoiding the mess of wiring inside the refrigerator. To avoid these challenges and to fill the research gap, this paper proposes an automated identification algorithm for Computer Vision in Smart Refrigerators using standard Convolutional Neural Network (CNN) architectures
The training accuracy results of previous related works using various CNN Models and datasets is depicted in Tab. 2
Summary
It comprises of three major blocks i.e., Intelligent Module section, refrigerator with attached display screen and cloud server. The role of intelligent module which is in form of a portable trolley system is camera scanning for food item identification and noting down the weight readings via load cell (label ‘D’) attached at the bottom of weight sensing area (label ‘F’). With the help of these two sub modules the name of food item recognized along with weight readings are obtained and further sent to cloud server as well as display screen attached to the refrigerator. A shopping list is automatically created of scarce items which get added in shopping list tab of the application
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