Multi-mosquito object detection and 2d pose estimation for automation of PfSPZ malaria vaccine production

Hongtao Wu, Jiteng Mu, Ting Da, Mengdi Xu, Russell H Taylor, Iulian Iordachita, Gregory S. Chirikjian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Multi-mosquito object detection and 2D pose estimation are essential steps towards fully automated extracting PfSPZ-infected mosquito salivary glands for manufacture of PfSPZ Vaccine, which has been shown to protect against malaria in multiple clinical trials in the US, Europe, and Africa. This paper presents a deep learning approach to perform cluster condition classification and bounding box detection of multiple mosquitoes in an image. It also estimates the 2D pose of each non-clustered mosquito by body part detection. This approach is based on two popular convolutional neural network (CNN) architectures, Mask R-CNN and DeeperCut. In addition, we propose a cascaded image processing approach to achieve the multi-mosquito detection, cluster condition classification, and body parts detection in a multi-step manner. We compare the two approaches in terms of their functionality, robustness, accuracy, and speed. We hope our effective approaches would push forward the automation of PfSPZ Vaccine production to facilitate the prevention and elimination of this disease worldwide.

Original languageEnglish (US)
Title of host publication2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019
PublisherIEEE Computer Society
Pages411-417
Number of pages7
ISBN (Electronic)9781728103556
DOIs
Publication statusPublished - Aug 1 2019
Event15th IEEE International Conference on Automation Science and Engineering, CASE 2019 - Vancouver, Canada
Duration: Aug 22 2019Aug 26 2019

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2019-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference15th IEEE International Conference on Automation Science and Engineering, CASE 2019
CountryCanada
CityVancouver
Period8/22/198/26/19

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ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Wu, H., Mu, J., Da, T., Xu, M., Taylor, R. H., Iordachita, I., & Chirikjian, G. S. (2019). Multi-mosquito object detection and 2d pose estimation for automation of PfSPZ malaria vaccine production. In 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019 (pp. 411-417). [8842953] (IEEE International Conference on Automation Science and Engineering; Vol. 2019-August). IEEE Computer Society. https://doi.org/10.1109/COASE.2019.8842953