DASS-Net: A Multi-Branch Aesthetic–Spectral–Semantic Deep Learning Model for Food Spoilage Detection in Hospitality Operations - Descarten, Randy O.; Treceñe, Jasten Keneth D. (Journal of Innovative Image Processing, 2025)
Food freshness and food safety in the hospitality industry are, to date, mainly dependent on visual observation, where human visual interpretation does not always successfully identify the less obvious signs of early food spoilage. In this work, we introduce DASS-Net, a novel hybrid deep network with a trilinear structure and three separate task-focused branches: (1) the RGB aesthetic branch, (2) the LAB spectral branch, and (3) the Vision Transformer ViT-Small semantic branch, used together for simultaneous analysis of food image-based texture degradation, color deviation, and overall food image-related semantic dependencies on a global scale. Each of the network’s task-focused branches projects its outputs to a unitary latent space and further refines them with multi-head attention and adaptive joint processing with a learnable gated fusion module. To train DASS-Net, a dual-objective training approach with class-weighted and smoothed Cross-Entropy loss and Contrastive Learning with MixUp and CutMix data augmentations is used. Eight-class classification experiments on fresh and spoiled food samples of bread, dairy products, fruits, and vegetables are conducted to evaluate the performance of DASS-Net, providing a validation accuracy of 94.33%, which demonstrates a 23.18% and 25.83% relative improvement over ResNet-18 with LAB and RGB visual channels, respectively. Additionally, the classification model yielded a minimum misclassification rate of 13.30% for spoiled items identified as fresh, which clearly marks an important characteristic related to safety issues. The complementarity between the spectral and aesthetic features has also been proven by the Grad-CAM visualizations of the dual-branch network, where the most confusion occurs in the case of dairy products with negligible surface decay, as revealed by the analysis of failure cases. The results explicitly confirm that the developed DASS-Net delivers a safe, efficient, and scalable vision solution applicable to kitchen intelligence and hospitality control scenarios.