Mr. Sangjun Lee Profile

Sangjun Lee

at Chungnam Univ

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Proceedings Article | 30 May 2022 Presentation

Deep learning-based image analysis approach for mosquito identification

Sangjoon Lee, Hangi Kim, Byoung-Kwan Cho

Proceedings Volume PC12120, PC1212005 (2022) https://doi.org/10.1117/12.2621176

KEYWORDS: Image analysis, RGB color model, Performance modeling, Optical microscopy, Luminescence

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Mosquitoes are the most life-threatening insect to human on Earth. Main disease vector mosquitoes inhabiting in Korea cause Zika fever, Yellow fever, Malaria, West Nile fever, Japanese encephalitis, etc. Only Malaria has cure among them. Usually, the disease vector mosquito species are counted and identified manually with optical microscopy, which needs huge labor and causes human error. Although the recent mosquito trap devices are developed, they can only count the number of mosquitoes without the species identification. This study proposes a deep learning image analysis technique to identify species along with the population of mosquitoes using SWIN-transformer model. The non-maximum suppression (NMS) technique for both RGB and fluorescence images has been applied for the improvement of prediction accuracy. Results revealed that the proposed model has achieved good performance for mosquito identification.

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