Oropharynx visual detection by using a multi-attention single-shot multibox detector for human–robot collaborative oropharynx sampling

Qing Gao, Yongquan Chen, Zhaojie Ju

Research output: Contribution to journalArticlepeer-review


The pandemic of COVID-19 has increased the demand for the oropharynx sampling robots. For an automatic oropharynx sampling, detection and localization of the oropharynx objects are essential. First, in response to the small-object and real-time needs of visual oropharynx detection, a lightweight multi-attention single-shot multibox detector (MASSD) method is designed. This method can effectively improve the detection accuracy of oropharynx sampling regions, especially small regions, while ensuring sufficient speed by introducing spatial attention, channel attention, and feature fusion mechanisms into the single-shot multibox detector. Second, the proposed MASSD is applied to an oropharyngeal swab (OP-swab) robot system to detect oropharynx sampling regions and conduct autonomous sampling. In the experiment, training and validation based on a custom oropharynx dataset verify the effectiveness and efficiency of the proposed MASSD. The detection accuracy can reach 81.3% of mean average precision@0.5:0.95 at 104 frames per second and the application experiment on the OP-swab robot system performs oropharynx sampling with 100% success accuracy in human–robot collaboration strategy.
Original languageEnglish
Journal IEEE Transactions on Human-Machine Systems
Early online date1 Nov 2023
Publication statusEarly online - 1 Nov 2023


  • oropharynx detection
  • OP-swab robot
  • COVID-19

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