用轻量模型让手机实时识别垃圾，省电又环保。
DWaste: Greener AI for Waste Sorting using Mobile and Edge Devices
- 用轻量检测模型在手机上实现垃圾实时识别
- 最快0.03秒推理，模型小于7MB，mAP达80%
- 量化压缩使模型体积和显存降低75%，适合低功耗设备
便利包装的普及导致垃圾量激增，高效垃圾分类对可持续管理至关重要。为此，我们开发了DWaste，一个基于计算机视觉的平台，可在资源受限的智能手机和边缘设备上实现离线实时垃圾分类。我们使用自建的回收数据集，测试了多种图像分类模型（EfficientNetV2S/M、ResNet50/101、MobileNet）和目标检测模型（YOLOv8n、YOLOv11n），包括我们提出的YOLOv8n-CBAM。结果显示：高效分类器EfficientNetV2S准确率约96%，但延迟高达0.22秒，碳排放高；而轻量级检测模型最高可达80% mAP，推理仅需0.03秒，模型大小低于7MB，更适合实时低功耗场景。模型量化进一步提升效率，使模型尺寸和显存使用减少最多75%。本工作验证了‘绿色AI’在边缘设备上实现可持续垃圾分类的可行性。
原文摘要 · Abstract (English)
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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