Akademik Veri Yönetim Sistemi
17th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2025, Targoviste, Romanya, 26 - 27 Haziran 2025, (Tam Metin Bildiri)
Effective inventory tracking plays a crucial role in facility management by optimizing resource allocation, reducing operational costs, and minimizing manual effort. This study conducts a comparative analysis of multiple YOLObased deep learning models YOLOv8, YOLOv9, YOLOv11, and YOLOv12 for office inventory detection and quantification. The primary objective is to assess the performance of these models in accurately identifying and counting office supplies from image data. Since standard YOLO architectures do not include predefined categories for office inventory, a custom dataset is developed. A total of 10,000 images of common office inventory items are gathered through web crawling, and 2,000 images are manually annotated using Roboflow to facilitate model training. The dataset comprises 14 distinct office inventory classes, ensuring a broad representation of essential items. Each YOLO version is evaluated based on detection accuracy, processing speed, and computational efficiency. Comparative experimental results reveal the strengths and limitations of each model, highlighting the trade-offs between precision and inference time. The findings provide valuable insights into the most suitable YOLO architecture for real-world facility management applications, contributing to the advancement of automated inventory tracking systems.