[2603.00368] Deep Learning-Based Meat Freshness Detection with Segmentation and OOD-Aware Classification

Computer Science > Machine Learning arXiv:2603.00368 (cs) [Submitted on 27 Feb 2026] Title:Deep Learning-Based Meat Freshness Detection with Segmentation and OOD-Aware Classification Authors:Hutama Arif Bramantyo, Mukarram Ali Faridi, Rui Chen, Clarissa Harris, Yin Sun View a PDF of the paper titled Deep Learning-Based Meat Freshness Detection with Segmentation and OOD-Aware Classification, by Hutama Arif Bramantyo and 4 other authors View PDF HTML (experimental) Abstract:In this study, we present a meat freshness classification framework from Red-Green-Blue (RGB) images that supports both packaged and unpackaged meat datasets. The system classifies four in-distribution (ID) meat classes and uses an out-of-distribution (OOD)-aware abstention mechanism that flags low-confidence samples as No Result. The pipeline combines U-Net-based segmentation with deep feature classifiers. Segmentation is used as a preprocessing step to isolate the meat region and reduce background, producing more consistent inputs for classification. The segmentation module achieved an Intersection over Union (IoU) of 75% and a Dice coefficient of 82%, producing standardized inputs for the classification stage. For classification, we benchmark five backbones: Residual Network-50 (ResNet-50), Vision Transformer-Base/16 (ViT-B/16), Swin Transformer-Tiny (Swin-T), EfficientNet-B0, and MobileNetV3-Small. We use nested 5x3 cross-validation (CV) for model selection and hyperparameter tuning. On the held-out I...