Resource Constrained Semantic Segmentation for Waste Sorting
Elisa Cascina Andrea Pellegrino Lorenzo Tozzi
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.