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Automation Control in Retail Using Deep Learning Algorithms

Student: Diakin Nikolai

Supervisor: Sergey Lisitsyn

Faculty: Graduate School of Business

Educational Programme: Big Data Systems (Master)

Year of Graduation: 2019

At present, the central place for producers of goods and retailers themselves is the store shelf. The right products, their location and fullness is the main aspect to meet the needs of consumers at a particular time and thus the growth of profits for the companies themselves and retails. The main problem for producers and retail is an audit that is performed manually and has incomplete coverage throughout the day. With the development and availability of computing capacity, it becomes possible to process a massive amount of data and train models based on hundreds of thousands of samples to automate processes. Using the methods of machine learning, it turns out to solve many automation problems for both producers and retail themselves for audit during the day. This paper does the analysis the main issues controlling in retail and presents an implementation model of computer vision for detecting and classifying objects to figure out violations the rules in merchandising and can solve automation issue for audit. Also, I designed/implemented architecture for a system that uses distributed and async approach for scalability and performance

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