Medical waste contains lots of viruses and bacteria.To intelligently sort medical waste from the source,an in-telligent sorting platform based on machine vision and the Delta mechanism was developed,and a three-stage multiob-jective 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 mod-el was used to manage the classification rules.In the third stage,the positioning sorting algorithm was used to guide tar-get positioning and grabbing.For the sorting prototype integrated with the MWRIS algorithm,2 217 medical sample im-ages 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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