DWaste: Greener AI for Waste Sorting using Mobile and Edge Devices

TL;DR

DWaste is a computer vision platform enabling real-time, sustainable waste sorting on mobile and edge devices, balancing accuracy and resource efficiency through model benchmarking and optimization.

Contribution

The paper introduces DWaste, a novel platform that benchmarks and optimizes AI models for efficient, real-time waste sorting on resource-constrained devices, including offline capabilities.

Findings

Lightweight models achieve high speed and small size.

Quantization reduces model size and VRAM usage by up to 75%.

EfficientNetV2S offers high accuracy (~96%) but with higher latency.

Abstract

The rise of convenience packaging has led to generation of enormous waste, making efficient waste sorting crucial for sustainable waste management. To address this, we developed DWaste, a computer vision-powered platform designed for real-time waste sorting on resource-constrained smartphones and edge devices, including offline functionality. We benchmarked various image classification models (EfficientNetV2S/M, ResNet50/101, MobileNet) and object detection (YOLOv8n, YOLOv11n) including our purposed YOLOv8n-CBAM model using our annotated dataset designed for recycling. We found a clear trade-off between accuracy and resource consumption: the best classifier, EfficientNetV2S, achieved high accuracy(~ 96%) but suffered from high latency (~ 0.22s) and elevated carbon emissions. In contrast, lightweight object detection models delivered strong performance (up to 80% mAP) with ultra-fast inference (~ 0.03s) and significantly smaller model sizes (< 7MB ), making them ideal for real-time, low-power use. Model quantization further maximized efficiency, substantially reducing model size and VRAM usage by up to 75%. Our work demonstrates the successful implementation of "Greener AI" models to support real-time, sustainable waste sorting on edge devices.