WasNet: A Neural Network-Based Garbage Collection Management System
Zhihu Yang, Dan Li
Abstract
In response to the increasing pollution caused by unseparated garbage, classification systems for garbage separation have become very popular. First, we constructed a complex data augmentation combination for model training. Second, we designed a novel lightweight neural network garbage classification system called WasNet. This proposed network's 1.5 million parameters on the ImageNet dataset are one-half of mainstream neural networks, while at 3 million floating point operations per second (FLOPs) it is one third of mainstream neural networks that have obtained the best performance among known lightweight neural networks. The accuracy on the ImageNet data set is 64.5%, on the Garbage Classification dataset it is 82.5%, and on the TrashNet dataset it is 96.10%. Furthermore, we transplanted the model to the hardware platform and assembled an intelligent trash can; we developed a garbage recognition application to facilitate users to directly identify and receive platform information; we built a visualization and decision support platform to help managers monitor traffic in real time. We combined the intelligent trash can, application, visualization and decision-making platform into a system, which is the most complete and effective system among the known research works. The results of the test we conducted on our platform using our extended dataset showed that our scheme is very reliable. At the same time, we also open source our extended datasets for use by other researchers.