A new deep learning based object detection system for increasing salesman performance

Food distribution companies must be flexible and responsive to provide the best services to their customers. Operational costs and expenses are the biggest concern for these companies, as they rely on many employees to serve customers and achieve the company's goals. Increasing tasks creates continuous and growing pressure on the sales team and the rest of its supporting departments, which affects the time and effort of the salesperson in achieving daily tasks and thus achieving the expected monthly goal. Deep learning technologies provide solutions to help reduce the effort expended by the sales team and, at the same time, contribute to reducing operating costs. In this thesis, an integrated system is proposed to use deep learning techniques to reduce the representative's effort and compensate the human support staff with deep learning algorithms to provide the necessary support to the representative. YOLO algorithms are used to discover objects in real-time and Faster-RCNN algorithm is used to solve planogram issues on shelf. The results extracted from images are used from these algorithms and converted into data stored in the MySQL database. Google API techniques are used to transfer the results from the cloud to the company's servers and thus analyze the results received using the Microsoft Power BI tool. The results show that proposed system provides a complete solution to reduce the effort of the sales team and significantly reduce costs. Also, this system can be used to study the competitor productions visibility on shelves.