Enhanced Segmentation of Deformed Waste Objects in Cluttered Environments

Muhammad Ali, Omar Alsuwaidi, Salman Khan

2024

Abstract

Recycling is a crucial process for mitigating environmental pollution; however, due to inefficiencies in waste sorting, a significant portion of recyclable waste is being underutilized. The complexity and disorganization of waste streams make it challenging to efficiently separate recyclable materials. Identifying recyclable items in cluttered environments requires the recognition of highly deformable objects by computer vision systems. To this end, we propose a computer vision-based approach capable of efficiently separating recyclable materials from waste, even in disorganized settings, by recognizing highly deformable objects. We extend an existing large-scale CNN-based model, the InternImage, by introducing Mutli-scale networks and combining cross-entropy and dice loss for improved segmentation. Our focus is on enhancing the segmentation of the ZeroWaste-f dataset, an industrial-grade dataset for waste detection and segmentation. We further propose a unique Mutli-scale feed-forward network configuration and integrate it with the InternImage architecture to effectively model Multi-scale information on the challenging ZeroWaste-f dataset for both waste detection and segmentation tasks. This improvement is further enhanced by introducing a novel Freezeconnect module which helps to counteract neuron co-adaptation during training by redistributing the learning (gradient signal) across the network. We compare our model with existing state-of-the-art baseline methods on ZeroWaste-f and TrashCAN datasets to demonstrate the effectiveness of our method.

Paper Citation

in Harvard Style

Ali M., Alsuwaidi O. and Khan S. (2024). Enhanced Segmentation of Deformed Waste Objects in Cluttered Environments. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 570-581. DOI: 10.5220/0012424900003654

in Bibtex Style

@conference{icpram24,
author={Muhammad Ali and Omar Alsuwaidi and Salman Khan},
title={Enhanced Segmentation of Deformed Waste Objects in Cluttered Environments},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2024},
pages={570-581},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012424900003654},
isbn={978-989-758-684-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Enhanced Segmentation of Deformed Waste Objects in Cluttered Environments
SN - 978-989-758-684-2
AU - Ali M.
AU - Alsuwaidi O.
AU - Khan S.
PY - 2024
SP - 570
EP - 581
DO - 10.5220/0012424900003654
PB - SciTePress