Yolov9 Based Real Time Culinary Equipment Detection to Automate The Cooking System

As technology keeps growing, people are getting busier and relying more on machines, which means they have less time for cooking and other household tasks. The field of computer vision is always changing, aiming to make interactions between people and machines more comfortable. One important part of computer vision is recognizing objects, even if they’re only partly visible or close to something else. This research investigates the use of the YOLOv9 model to enhance object detection in kitchen environments, specifically focusing on the accurate recognition of culinary tools and equipment. Object detection is critical for advancing automation in kitchen technology, allowing machines to identify and interact with tools such as knives, plates, spoons, cups and blender in real-time. Our method employs the "You Only Look Once" (YOLO) approach, enabling fast and precise detection by processing images in a single forward pass. Trained on a comprehensive dataset of kitchen tools, the YOLOv9 model achieved a mean Average Precision (mAP) of 78.1% when tested on unseen data. Additionally, an optimized hardware setup was implemented to ensure efficient processing, making the model well-suited for real-world kitchen automation applications. This research marks a significant contribution to the field of computer vision for culinary tasks, offering a reliable and robust solution for object detection in automated cooking systems.