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CoGY-Net: An Efficient AI-Powered Shelf OOS Detection System | ||
| Computational Sciences and Engineering | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 24 بهمن 1404 | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22124/cse.2026.32660.1150 | ||
| نویسندگان | ||
| Mohsen Eshghanmalek1؛ Vali Derhami* 2؛ Mohammad Ghasemzadeh1 | ||
| 1Department of Computer Engineering, Faculty of Engineering, Yazd University, Yazd, Iran. | ||
| 2Yazd University, Department of Computer Engineering | ||
| چکیده | ||
| 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-23 dataset. Experimental results demonstrate that CoGY-Net achieves an accuracy 90% 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. | ||
| کلیدواژهها | ||
| Contourlet Transform؛ Golden Eagle Optimizer؛ YOLO Neural Network؛ Deep Learning؛ Out of Stock Detection | ||
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آمار تعداد مشاهده مقاله: 17 |
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