Empty Spaces Detection in Shelf Data Computer Vision Project

Effective inventory management is crucial for modern retailers to meet customer demands and optimize operational efficiency. Detecting empty spaces on store shelves is a critical aspect of this process, traditionally reliant on manual efforts that are time-consuming and error-prone. However, the emergence of computer vision and deep learning presents an opportunity to revolutionize this practice. This project explores the application of YOLOv8, an advanced object detection algorithm, to automate the identification of vacant shelf spaces. By harnessing YOLOv8's capabilities, retailers can modernize inventory management, leading to more informed decision-making and improved customer experiences.

The project's main goal is to develop and train a YOLOv8 model for precise detection and localization of empty spaces within shelves. The workflow includes data preprocessing, model architecture selection, hyperparameter tuning, and thorough evaluation. The trained YOLOv8 model offers real-time, high-precision empty space detection, enabling retailers to enhance stock management and operational efficiency. This automation eliminates manual monitoring, providing proactive inventory insights and empowering retailers to optimize store layouts and stock levels. Throughout the project, we delve into the complexities of training and evaluating object detection models, offering insights into implementing state-of-the-art solutions in real-world retail scenarios.