Efficient Dataset Generation for Stacked Meat Products Instance Segmentation in Food Automation

Dear Editor, This letter presents techniques to simplify dataset generation for instance segmentation of raw meat products, a critical step toward automating food production lines. Accurate segmentation is essential for addressing challenges such as occlusions, indistinct edges, and stacked configurations, which demand large, diverse datasets. To meet these demands, we propose two complementary approaches: a semi-automatic annotation interface using tools like the segment anything model (SAM) and GrabCut and a synthetic data generation pipeline leveraging 3D-scanned models. These methods reduce reliance on real meat, mitigate food waste, and improve scalability. Experimental results demonstrate that incorporating synthetic data enhances segmentation model performance and, when combined with real data, further boosts accuracy, paving the way for more efficient automation in the food industry.