Towards Effective Waste Segmentation for Automated Waste Recycling in Cluttered Background

2026-06-11

AI Summary

This work addresses the limitations of existing automatic waste recycling systems, which suffer from degraded segmentation performance in cluttered environments and rely on computationally expensive backbone networks that hinder efficient deployment. To overcome these challenges, we propose a spatial-spectral dual-domain cascaded segmentation network that models local structural dependencies in the spatial domain while capturing global contextual relationships in the spectral domain. Furthermore, an Auxiliary Feature Enhancement Module (AFEM) is introduced to reinforce object boundaries and regional semantics. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art approaches on the ZeroWaste-aug, ZeroWaste-f, and SpectralWaste datasets, achieving higher accuracy and robustness in waste segmentation under cluttered conditions while maintaining computational efficiency.

Abstract

Rapid expansion of urban areas and population growth is causing an immense increase in waste production, which demands the need for efficient and automated waste management. In this scenario, automated waste recycling (AWR) using deep learning methods can assist humans in optimal waste management. Recent deep learning approaches for AWR provide promising waste segmentation performance, however, these methods rely on large backbone networks that are inefficient for AWR systems and suffer from performance deterioration in cluttered scenes. To this end, an optimal waste segmentation network is introduced which effectively utilizes the spatial domain to capture localized structural dependencies and the spectral domain to efficiently extract global contextual relationships. This cascaded design allows the network to progressively leverage both local and global representations across complementary domains to highlight the semantic information necessary for effective segmentation of various waste objects. Furthermore, auxiliary feature enhancement module (AFEM) is introduced to enhance the target objects' boundaries and blob amplification for better segmentation in cluttered scenarios. Extensive experimentation on ZeroWaste-aug, ZeroWaste-f and SpectralWaste datasets reveals the merits of the proposed method.

Problem

• waste segmentation
• automated waste recycling
• cluttered background
• semantic segmentation
• deep learning

Innovation

• waste segmentation
• spatial-spectral fusion
• cascaded network design
• auxiliary feature enhancement module
• cluttered background