# Resource Constrained Semantic Segmentation for Waste Sorting

Source: [Resource Constrained Semantic Segmentation for Waste Sorting](<https://www.sota2.com/research/paper/68fa71061e209131cea4ea51>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:55:42.510Z

Authors: Elisa Cascina, Andrea Pellegrino, Lorenzo Tozzi

Published: 2023-10-30T10:19:40.000Z

## Abstract

This work addresses the need for efficient waste sorting strategies in Materials Recovery Facilities to minimize the environmental impact of rising waste. We propose resource-constrained semantic segmentation models for segmenting recyclable waste in industrial settings. Our goal is to develop models that fit within a 10MB memory constraint, suitable for edge applications with limited processing capacity. We perform the experiments on three networks: ICNet, BiSeNet (Xception39 backbone), and ENet. Given the aforementioned limitation, we implement quantization and pruning techniques on the broader nets, achieving positive results while marginally impacting the Mean IoU metric. Furthermore, we propose a combination of Focal and Lov\\'asz loss that addresses the implicit class imbalance resulting in better performance compared with the Cross-entropy loss function.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2310.19407>)

[PDF](<https://arxiv.org/pdf/2310.19407.pdf>)