摘要
医疗垃圾中存在大量的病毒和细菌,为解决医疗垃圾源头智能分类问题,开发了基于机器视觉和Delta机构的智能分拣平台样机,并提出一种三阶段的多目标医疗垃圾识别分拣(medical waste recognition-indexes-sorting,MWRIS)算法。第1阶段提出数据增强扩容的IE-YOLOv4算法建立起医疗垃圾识别模型,与Faster R-CNN、RetinaNet、CenterNet等5种模型比较;第2阶段索引分类模型用于管理分类规则;第3阶段定位分拣算法指导目标定位分拣。在集成了MWRIS算法的分拣样机上,采集14种,2217张医疗样本图像,完成医疗垃圾分拣实验。结果表明,使用IE-YOLOv4的MWRIS算法对医疗垃圾识别准确率显著提升至99.30%,分拣实验对目标定位准确率达到96.17%,最终分类正确率为86.67%,验证了多目标医疗垃圾识别分拣系统的有效性。
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.
作者
张歆羽
杨钟亮
周哲画
张凇
毛新华
ZHANG Xinyu;YANG Zhongliang;ZHOU Zhehua;ZHANG Song;MAO Xinhua(School of Mechanical Engineering,Donghua University,Shanghai 201620,China;Qingdao Virtual Reality Institute Co.,Ltd.,Qingdao 266100,China;Manchester University,Manchester M139PL;Beijing Chonglee Machinery Engineering Co.,Ltd.,Beijing 101111,China)
出处
《智能系统学报》
CSCD
北大核心
2024年第3期584-597,共14页
CAAI Transactions on Intelligent Systems
基金
国家自然科学基金项目(51905175)
浙江省健康智慧厨房系统集成重点实验室开放基金项目(2014E10014).
关键词
机器视觉
目标检测
Delta分拣系统
机械设计
人工智能
医疗垃圾
垃圾分类
智能垃圾箱
machine vision
object detection
Delta sorting system
mechanical design
artificial intelligence
medical waste
garbage classification
intelligent dustbin
