Project Introduction

The project aims to use computer vision technologies, machine learning, deep learning and artificial intelligence technologies to increase the operational efficiency of the restaurant and catering industry.
It contains the following setups for different operations of table service and quick service restaurants;


In fast service restaurants where the customer buys their own food; The food and beverages purchased by the customer coming in front of the cash register will be detected by image recognition and automatically entered into the point of sale system (POS). At this point, the data entry of the cashier will be minimized and operational efficiency will be increased.



In table service restaurants, the environment will be monitored with the camera, and it will be determined by image recognition that the tables are full/empty, dirty/clean and whether the meal is finished or not. All these situations will be analyzed with the restaurant management system and necessary warnings will be given to the employees who manage the operation. Thus, it is aimed to increase operational efficiency.

Within the scope of the project, the data obtained through image processing and recognition will be transferred to the Cloud POS system developed by Protel and supported by Teydeb 1501. byteLAKE’s Machine Learning / Artificial Intelligence modules will perform images recognition and the results will be processed with the historical sales data in the POS system. In this way, the system will automatically decide on the food to be added to the customer’s account with deep learning and artificial intelligence.

The Innovative Side of the Project

  • Managing the operation using computer vision, machine learning and artificial intelligence technologies is an issue that has not yet been applied in the restaurant industry.
  • In addition, when the project output is started to be used by businesses, classified and labeled food and beverage data will be collected from many different restaurants.
  • The use of a big data source in this detail in machine learning and deep learning methods, especially for food and beverage, will be the first in the field.
  • At the same time, this scale of data will form an infrastructure for different applications. The system to be realized will be directly integrated into the POS systems and efficiency will be increased in the sector.
  • A food image library will be established.

Project Name


Project Number


Project Start Date

1 February 2020

Project Completion Date

29 July 2022

Project Duration

30 months

Project Partners


GTÜ TTO : Dr. Faculty Member
Yakup Genç

Computer Engineering