Introduces the SteelDS dataset with 24,297 annotated frames of E40 steel and copper scrap for object detection and instance segmentation to aid industrial sorting.
Taco: Trash annotations in context for litter detection,
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Cascaded spatial-spectral segmentation network with AFEM module claimed to improve waste object segmentation on ZeroWaste-aug, ZeroWaste-f and SpectralWaste datasets in cluttered backgrounds.
Empirical comparison of OvA and OvR strategies with confidence-based human-in-the-loop on a municipal waste classification dataset aligned to Goslar rules.
citing papers explorer
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SteelDS: A High-Resolution Video Dataset of E40 Steel Scrap for Object Detection and Instance Segmentation
Introduces the SteelDS dataset with 24,297 annotated frames of E40 steel and copper scrap for object detection and instance segmentation to aid industrial sorting.
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Towards Effective Waste Segmentation for Automated Waste Recycling in Cluttered Background
Cascaded spatial-spectral segmentation network with AFEM module claimed to improve waste object segmentation on ZeroWaste-aug, ZeroWaste-f and SpectralWaste datasets in cluttered backgrounds.
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Efficient Waste Sorting for Circular Economy: A Confidence-guided comparison between One-Vs-All and One-Vs-Rest Classification Strategies with Human-in-the-Loop for Automated Waste Sorting
Empirical comparison of OvA and OvR strategies with confidence-based human-in-the-loop on a municipal waste classification dataset aligned to Goslar rules.