Journal of the Korean Society for Industrial and Applied Mathematics

Korean Society for Industrial and Applied Mathematics (한국산업응용수학회)
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ANOMALY DETECTION FOR AN ORAL HEALTH CARE APPLICATION USING ONE CLASS YOLOV3

  • Received : 2022.11.18
  • Accepted : 2022.12.07
  • Published : 2022.12.25
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Abstract

In this report, we apply an anomaly detection algorithm to a mobile oral health care application. In particular, we have investigated one class YOLOv3 as an anomaly detection model to classify pictures of mouths which will be used as inputs in the following machine learning model. We have achieved outstanding performances by proposing appropriate annotation strategies for our data sets and modifying the loss function. Moreover, the model can classify not only oral and non-oral pictures but also output preprocessed pictures that only contain the area around the lips by using the predicted bounding box. Thus, the model performs prediction and preprocessing simultaneously.

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Acknowledgement

SK was supported by National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (No. 2020R1A5A1016126, 2022R1C1C2004559), and DW was supported by Government-funded Technology Commercialization Program through the Korea Institute for Advancement of Technology (KIAT) funded by the Ministry of Trade, Industry and Energy (MOTIE) (No. P145600042).

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