CoGY-Net: An Efficient AI-Powered Shelf OOS Detection System

In modern retail management, there is a high demand for being able to efficiently identify empty shelves. To address the inherent limitations of current monitoring systems in handling dense arrangements and geometrically diverse products, To address this image processing problem, we propose CoGY-Net—a robust, intelligent Out-of-Stock (OOS) detection framework that takes RGB images of retail shelves as input. Our approach significantly enhances the standard YOLO architecture through two primary innovations. First, we integrate the Contourlet Transform as a geometric pre-processor to improve the extraction of curved product features within cluttered backgrounds, leveraging its superior directionality over traditional transforms. Second, we employ the Golden Eagle Optimizer (GEO), a metaheuristic algorithm, to eliminate the inefficiency of manual tuning by autonomously identifying the ideal training hyperparameters and anchor boxes tailored to the specific dataset. Furthermore, to ensure the system remains reliable across varying shelf depths, we implemented a scale-invariant dynamic gap analysis logic to pinpoint empty spaces accurately. In this way we manage to fill the research gap which was the lack of an automated and geometry-aware detection framework capable of handling dense shelf layout and curved products in real environments. The system was evaluated on the Out-Of-Stock-۲۳ dataset. Experimental results demonstrate that CoGY-Net achieves an accuracy ۹۰% and provides a high-precision, automated solution with superior stability, making it highly suitable for seamless integration into real-time smart retail environments and autonomous inventory systems.

Keywords:

Contourlet Transform , Golden Eagle Optimizer , YOLO Neural Network , Deep learning , Out of Stock Detection