[1]ZHANG Xinyu,YANG Zhongliang,ZHOU Zhehua,et al.Design of an intelligent identification and sorting system used for classification of multiobjective medical waste[J].CAAI Transactions on Intelligent Systems,2024,19(3):584-597.[doi:10.11992/tis.202204039]
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Design of an intelligent identification and sorting system used for classification of multiobjective medical waste

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 3 Page number: 584-597 Column: 学术论文—机器感知与模式识别 Public date: 2024-05-05
Title:
Design of an intelligent identification and sorting system used for classification of multiobjective medical waste
Author(s):
ZHANG Xinyu1; 2;  YANG Zhongliang1;  ZHOU Zhehua1;  ZHANG Song3;  MAO Xinhua4
1. School of Mechanical Engineering, Donghua University, Shanghai 201620, China;
2. Qingdao Virtual Reality Institute Co., Ltd., Qingdao 266100, China;
3. Manchester University, Manchester M13 9PL;
4. Beijing Chonglee Machinery Engineering Co., L
Keywords:
machine vision; object detection; Delta sorting system; mechanical design; artificial intelligence; medical waste; garbage classification; intelligent dustbin
CLC:
TP241.3；TP391.41
DOI:
10.11992/tis.202204039
Abstract:
Medical waste contains lots of viruses and bacteria. To intelligently sort medical waste from the source, an intelligent sorting platform based on machine vision and the Delta mechanism was developed, and a three-stage multiobjective recognition-indexes-sorting (MWRIS) algorithm was proposed. In the first stage, the IE-YOLOv4 algorithm of data enhancement and expansion was proposed to establish a medical waste identification model, which was compared with five models, including Faster R-CNN, RetinaNet, and CenterNet. In the second stage, the index classification model was used to manage the classification rules. In the third stage, the positioning sorting algorithm was used to guide target positioning and grabbing. For the sorting prototype integrated with the MWRIS algorithm, 2217 medical sample images of 14 kinds were collected, and the medical waste sorting experiment was completed. The results showed that the MWRIS algorithm using IE-YOLOv4 can significantly improve the accuracy of medical waste identification to 99.30%, the accuracy rate of target positioning in the sorting experiment reaches 96.17%, and the final classification accuracy reaches 86.67%, verifying the effectiveness of the proposed medical waste identification and sorting system.
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