End-to-end solution for automatic beverage stock detection in supermarkets based on image processing and convolutional neural networks

This study addresses the challenge of detecting and identifying stock shortages in large warehouses through an advanced algorithm that integrates image processing and artificial intelligence techniques. Presently, many companies contend with the limitations of manual inventory management, such as susceptibility to errors, slow inventory actualizations, and consequent adverse economic effects. In contrast to solutions based on robotics, the proposed approach continuously monitors shelves throughout warehouse aisles using several fixed cameras, each connected to a single-board computer that processes the acquired images, identifies stock levels using deep learning, and updates a centralized database with stock analysis results. The algorithmic process begins with an image validation step based on a convolutional neural network to ensure obstacle-free images of the shelves. Subsequently, an application-specific YOLOv2 detector trained via transfer learning identifies product types captured in the images and estimates their stock levels. The proposed solution not only reduces the need for manual intervention and operational costs but also drastically enhances inventory supervision efficiency. The fully implemented system achieves an average accuracy of over 98 %, surpassing the human visual inspection performance. The proposed solution also incorporates the aspect of user-friendliness through a developed mobile application. This application connects to the centralized database, allowing inventory supervisors to receive alerts when the stock of a product falls below a user-configured threshold. This technological integration within a centralized system signifies a substantial advancement in inventory management, offering prompt responses to product scarcity situations and optimizing warehouse operational efficiency.