Shelf Product Detection Using Binarized Region Widths

The patent applies local quality by creating store-specific detection conditions that adapt to each store's unique shelf characteristics. The system learns the appropriate gap interval for each individual store based on their specific shelf configuration, rather than using a universal detection threshold. This allows the automated detection system to maintain high precision by accounting for local variations in shelf intervals across different stores.

The patent implements preliminary action by pre-learning and storing the appropriate gap intervals for each store before actual product detection begins. The system performs this learning phase in advance to establish store-specific detection parameters, so that during routine operation, the system can efficiently compare actual shelf gaps against these pre-established standards without complex real-time adjustments, thereby maintaining both simplicity and accuracy.

The patent applies self-service by enabling the system to automatically learn and determine the appropriate gap intervals for each store through image analysis, without requiring manual measurement or configuration by staff. The detection system autonomously captures shelf images, analyzes the actual product spacing, and establishes detection thresholds independently, thereby achieving store-specific precision while minimizing the time investment required from human operators.