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A smart municipal waste management system based on deep-learning and Internet of Things by Cong Wang; Jiongming Qin; Cheng Qu; Xu Ran; Chuanjun Liu; Bin Chen is a Environmental Science article available to read on EtoBox.

What is A smart municipal waste management system based on deep-learning and Internet of Things about?

A proof-of-concept municipal waste management system was proposed to reduce the cost of waste classification, monitoring and collection. In this system, we utilize the deep learning-based classifier and cloud computing technique to realize high accuracy waste classification at the beginning of garbage collection. To facilitate the subsequent waste disposal, we subdivide recyclable waste into plastic, glass, paper or cardboard, metal, fabric and the other recyclable waste, a total of six categories. Deep-learning convolution neural networks (CNN) were applied to realize the garbage classification task. Here, we investigate seven state-of-the-art CNNs and data pre-processing methods for waste classification, whose accuracies of nine categories range from 91.9 to 94.6% in the validation set. Among these networks, MobileNetV3 has a high classification accuracy (94.26%), a small storage size (49.5 MB) and the shortest running time (261.7 ms). Moreover, the Internet of Things (IoT) devices which implement information exchange between waste containers and waste management center are designed to monitor the overall amount of waste produced in this area and the operating state of any waste

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Author

Cong Wang; Jiongming Qin; Cheng Qu; Xu Ran; Chuanjun Liu; Bin Chen

Publisher

Elsevier BV

Published

2021

Language

EN

Field

Environmental Science (Physical Sciences)