WasteRefine: boundary-aware semantic segmentation of waste materials using a DINOv2 backbone with multi-scale feature fusion decoder

Abstract

The rapid increase in world waste production needs smart, data-driven frameworks for efficient material identification and sustainable resource management. Intelligent recycling systems and waste materials spontaneous segmentation often lack behind due to scarcity of proper annotated datasets, visual ambiguities and severe class imbalancement of rare objects. The research aims to propose WasteRefine, utilizing DINOv2 Vision Transformer backbone with boundary aware semantic segmentation and multi scale feature fusion decoder for waste materials. To capture and accumulate the global context, an advanced dense predictive transformer is used consisting top-down fusion of features, Pyramid Pooling Module, Squeeze and Excitation channel attention and boundary composition component, for the proper identification of cluttered, deformed and visually ambiguous waste objects. The paper also introduces WasteRefine dataset consisting of 2,213 annotated images across four different categories: paper, soft plastic, rigid plastic and metal, marking it as the first waste semantic segmentation dataset from Bangladesh which contains visuals across various regions and annotated precisely. The proposed framework is rigorously evaluated on three different dataset WasteRefine, ZeroWaste-F and SpectralWaste (RGB) and assessed across notable published baselines. The ViT-B achieved 96.64 ± 0.16% mIoU on WasteRefine dataset, 61.94 ± 0.84% mIoU on extremely class imbalanced and deformed ZeroWaste-F dataset and 70.73 ± 0.10% FG mIoU on SpectralWaste beating all the published reports. Competitive results of the ViT-S variant with only 25.16M parameters demonstrated efficient parameter count without severe performance degradation.