论文标题
智能回收箱，使用边缘的废物图像分类
A Smart Recycling Bin Using Waste Image Classification At The Edge
论文作者
论文摘要
快速的经济增长会导致对更有效的废物回收系统的紧急需求。因此，这项工作开发了一个创新的回收箱，自动将城市废物分开以提高回收率。我们收集了1800个回收废物图像，并将它们与现有的公共数据集相结合，以培训两个嵌入式系统Jetson Nano和K210的分类模型，以针对不同的市场。该模型的杰森·纳诺（Jetson Nano）的准确度为95.98％，K210的精度为96.64％。垃圾箱计划旨在收集用户的反馈。在Jetson Nano上，该应用程序的总体功耗从以前的工作降低了30％，至4.7 W，而第二个系统K210仅需要0.89 W的操作功率。总而言之，我们的工作证明了能源省力，高准确的智能回收箱的功能性完整的原型，将来可以将其商业化以改善城市废物回收。
Rapid economic growth gives rise to the urgent demand for a more efficient waste recycling system. This work thereby developed an innovative recycling bin that automatically separates urban waste to increase the recycling rate. We collected 1800 recycling waste images and combined them with an existing public dataset to train classification models for two embedded systems, Jetson Nano and K210, targeting different markets. The model reached an accuracy of 95.98% on Jetson Nano and 96.64% on K210. A bin program was designed to collect feedback from users. On Jetson Nano, the overall power consumption of the application was reduced by 30% from the previous work to 4.7 W, while the second system, K210, only needed 0.89 W of power to operate. In summary, our work demonstrated a fully functional prototype of an energy-saving, high-accuracy smart recycling bin, which can be commercialized in the future to improve urban waste recycling.
