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) using a subset of our own waste data set and annotated it using the custom tool Annotated Lab. 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 77% 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.

翻译：便利包装的兴起导致废弃物大量产生，使得高效的垃圾分类对于可持续废弃物管理至关重要。为此，我们开发了DWaste——一个基于计算机视觉的平台，专为资源受限的智能手机和边缘设备设计，支持实时垃圾分类并具备离线功能。我们使用自建废弃物数据集的一个子集，并借助定制标注工具Annotated Lab进行标注，对多种图像分类模型（EfficientNetV2S/M、ResNet50/101、MobileNet）与目标检测模型（YOLOv8n、YOLOv11n）进行了性能评估。研究发现准确率与资源消耗之间存在明显的权衡关系：最佳分类器EfficientNetV2S虽能达到较高准确率（约96%），但存在高延迟（约0.22秒）与高碳排放的问题。相比之下，轻量级目标检测模型在保持优异性能（最高77% mAP）的同时，具备超快推理速度（约0.03秒）和显著更小的模型体积（<7MB），非常适合实时低功耗场景。模型量化技术进一步提升了效率，使模型体积与显存占用最高降低75%。本研究证明了"绿色人工智能"模型在边缘设备上实现实时可持续垃圾分类的成功应用。