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Published Paper Details:
Synthetic Contamination Augmentation and Multi-Task Deep Learning for Robust Waste Material Recognition
Authors
Seanna Martin,Sumit Rathi,Sanjana Arun,Dr Sandeep Kulkarni
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Keywords
Waste Classification, Contamination Detection, Deep Learning, Transfer Learning, Multi-Task Learning, YOLOv8, MobileNetV2, Synthetic Data Augmentation, Computer vision, Recycling Automation.
Abstract
Pollution of recyclable waste through food remnants, liquid spills, grease among other foreign materials makes whole recycling batches unusable, causing enormous costs incurred in the recovery of the materials. Although contamination rates in single-stream recycling systems all over the world speak of more than 25 percent, virtually all automated waste classification studies in the global sphere take the waste altogether as contaminant-free. The system discussed in this paper is a deep learning system that fills this gap. Phase 1 (v1): the interacting multi-output dual-output MobileNetV2-based multi-task model is used to categorize waste material (cardboard, glass, metal, paper, plastic), and determine contaminated condition of an image at once. It presents a series of novel synthetic contamination pipelines that programmatically use spots of dirt, liquid stains, and texture atrophy to clean images, and produces a balanced 15 000-image training corpus of three publicly available datasets. v1 model attains 66.0 per cent category classification accuracy, 87.2 per cent contamination detection accuracy (AUC: 0.932) with an inference rate of 28 images per second. Phase 2 (v2) provides system upgrades to YOLOv8s with actual multi-object localization, that is, a separate bounding box of combined category-contamination labels to each object detected in a scene. V2 is trained on 10 merged classes and attains after 47 training epochs on NVIDIA RTX 4060 mAP50 of 0.855 with mAP50-95 of 0.851. A diagnostic of stain bias indicates that the accuracy of categories decreases by 7.7 percentage points over contaminated images at v1, which causes the architectural partition at v2. Collectively, these systems offer an open-source contamination aware automated recycling system on a scale.
IJCRT's Publication Details
Unique Identification Number - IJCRT2604101
Paper ID - 304760
Author type - Indian Author
Page Number(s) - a809-a821
Pubished in - Volume 14 | Issue 4 | April 2026
DOI (Digital Object Identifier) - https://doi.org/10.56975/ijcrt.v14i4.304760
No Of Downloads - 55
Author Country - India, 411048, Pune, Pune, 411048, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
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Seanna Martin,Sumit Rathi,Sanjana Arun,Dr Sandeep Kulkarni, "Synthetic Contamination Augmentation and Multi-Task Deep Learning for Robust Waste Material Recognition", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.14, Issue 4, pp.a809-a821, April 2026, Available at :http://www.ijcrt.org/papers/IJCRT2604101.pdf
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