DeepWaste：将深度学习应用于废物分类，实现可持续发展的地球
DeepWaste: Applying Deep Learning to Waste Classification for a Sustainable Planet
摘要：
准确的废物处理，在处置点，是应对气候变化的关键。当可以回收或堆肥的材料被转移到垃圾填埋场时，它们会导致甲烷等强效温室气体的排放。目前减少错误废物处理的尝试是昂贵的、不准确的和令人困惑的。在这项工作中，我们提出了一个易于使用的移动应用程序DeepWaste，它利用高度优化的深度学习技术为用户提供即时的垃圾分类，回收和堆肥。我们用几种卷积神经网络架构进行实验，以检测和分类废物。我们最好的模型是一个50层的深度学习残差神经网络，在测试集上达到了0.881的平均精度。我们在一组真实图像上展示了我们的应用程序的性能和效率。
英文摘要：
Accurate waste disposal, at the point of disposal, is crucial to fighting climate change. When materials that could be recycled or composted get diverted into landfills, they cause the emission of potent greenhouse gases such as methane. Current attempts to reduce erroneous waste disposal are expensive, inaccurate, and confusing. In this work, we propose DeepWaste, an easy-to-use mobile app, that utilizes highly optimized deep learning techniques to provide users instantaneous waste classification into trash, recycling, and compost. We experiment with several convolution neural network architectures to detect and classify waste items. Our best model, a deep learning residual neural network with 50 layers, achieves an average precision of 0.881 on the test set. We demonstrate the performance and efficiency of our app on a set of real-world images.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
