Deep learning-based image analysis approach for mosquito identification

Sangjoon Lee; Hangi Kim; Byoung-Kwan Cho

doi:10.1117/12.2621176

30 May 2022 Deep learning-based image analysis approach for mosquito identification

Sangjoon Lee, Hangi Kim, Byoung-Kwan Cho

Proceedings Volume PC12120, Sensing for Agriculture and Food Quality and Safety XIV; PC1212005 (2022) https://doi.org/10.1117/12.2621176
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Sangjoon Lee, Hangi Kim, and Byoung-Kwan Cho "Deep learning-based image analysis approach for mosquito identification", Proc. SPIE PC12120, Sensing for Agriculture and Food Quality and Safety XIV, PC1212005 (30 May 2022); https://doi.org/10.1117/12.2621176

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