ShelfSight: An AI-Driven Object Detection for Empty Shelf Analytics

ShelfSight aims to automate inventory management in retail stores by utilizing computer vision and deep learning techniques to detect empty spaces on store shelves. Traditional manual methods for monitoring shelf stock can be time-consuming and error-prone. By applying YOLOv8, an advanced object detection algorithm, we can revolutionize this process, providing real-time and accurate identification of vacant shelf spaces.

The key features include automated empty space detection and precise localization. The YOLOv8 model detects and localizes empty spaces on store shelves, eliminating the need for manual monitoring and reducing operational costs. The model operates in real-time, allowing for instantaneous detection and response to changes in shelf stock. YOLOv8 provides high-precision localization of empty spaces, offering valuable insights for optimizing shelf layouts and stock levels.