Abstract:
Street food demand is increasing day by day in Bangladesh. It has yummy tastes, easily
accessibility, low price, easily made, easy to available, attraction to the foods, and
above all, needs of the street people. There are many different classes of people from
many different areas, especially the middle class, poor and the lower-class people come
in Dhaka in search job for better earning. And their earning is so low that’s why they
can buy this type of street food because of low price. Mainly most of the young people
eating foods at the street and it is a fashion nowadays. This study has been detected of
these street foods can help people detect them. To conduct this study, we used Deep
Learning process to build our system of recognition various street cuisine. Deep
learning is a strong technology that has been used in a variety of fields to automate
fundamental procedures and improve the outcomes of these operations. A total of 3023
images with 14 items of street foods were used to detect. We conduct this captured
image and gained our expected feature by using image classification. For image
classification of street foods, we used TensorFlow algorithm. We also used
Convolutional Neural Network (CNN) for architecture and feature extraction of our
Model. The Convolutional Neural Network (CNN) achieved the accuracy of 97.72% ,
which is good and also giving us inspiration for our next research. Deep learning is
being utilized on the field and in the marketplace to boost yield and ensure the quality
of street food reaching consumers in the area of street food detection. In this thesis, we
planned to create a simple CNN that can recognize street food in images. This system
would help the human to reduce the time and effort needed for detecting of street foods
at street. We used sequential model to build our system. Deep Learning (DL) has
several applications due to its ability to learn robust representations from images.
Convolutional Neural Networks (CNN) is the main DL architecture for image
classification.
