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

• URL: http://arxiv.org/abs/2510.18513v2
• Date: Tue, 28 Oct 2025 16:44:35 GMT
• Title: DWaste: Greener AI for Waste Sorting using Mobile and Edge Devices
• Authors: Suman Kunwar,
• Abstract summary: DWaste is a computer vision-powered platform designed for real-time waste sorting on resource-constrained smartphones and edge devices.<n>Our work demonstrates the successful implementation of "Greener AI" models to support real-time, sustainable waste sorting on edge devices.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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