Automatic Detection and Classification System of Domestic Waste via Multimodel Cascaded Convolutional Neural Network

作     者：Li, Jiajia Chen, Jie Sheng, Bin Li, Ping Yang, Po Feng, David Dagan Qi, Jun 

作者机构：Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Samsung Elect China R&D Ctr Nanjing 210012 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Univ Sheffield Dept Comp Sci Sheffield S1 4DP S Yorkshire England Univ Sydney Sch Informat Technol Biomed & Multimedia Informat Technol Res Grp Sydney NSW 2006 Australia Xian Jiaotong Liverpool Univ Dept Comp Sci & Software Engn Suzhou 215123 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS》 (IEEE产业信息学汇刊)

年 卷 期：2022年第18卷第1期

页      面：163-173页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Natural Science Foundation of China [61872241, 61572316] Hong Kong Polytechnic University [P0030419, P0030929, P0035358, TII-20-4821] 

主　　题：Convolutional neural networks Object detection Informatics Predictive models Training Shape Recycling Detection precision domestic waste detection and classification multimodel cascaded convolutional neural network (MCCNN) smart trash can (STC) 

摘      要：Domestic waste classification was incorporated into legal provisions recently in China. However, relying on manpower to detect and classify domestic waste is highly inefficient. To that end, in this article, we propose a multimodel cascaded convolutional neural network (MCCNN) for domestic waste image detection and classification. MCCNN combined three subnetworks (DSSD, YOLOv4, and Faster-RCNN) to obtain the detections. Moreover, to suppress the false-positive predicts, we utilized a classification model cascaded with the detection part to judge whether the detection results are correct. To train and evaluate MCCNN, we designed a large-scale waste image dataset (LSWID), containing 30 000 domestic waste multilabeled images with 52 categories. To the best of our knowledge, the LSWID is the largest dataset on domestic waste images. Furthermore, a smart trash can is designed and applied to a Shanghai community, which helped to make waste recycling more efficient. Experimental results showed a state-of-the-art performance, with an average improvement of 10% in detection precision.