TY - GEN
T1 - Multi-mosquito object detection and 2d pose estimation for automation of PfSPZ malaria vaccine production
AU - Wu, Hongtao
AU - Mu, Jiteng
AU - Da, Ting
AU - Xu, Mengdi
AU - Taylor, Russell H.
AU - Iordachita, Iulian
AU - Chirikjian, Gregory S.
N1 - Funding Information:
We acknowledge the effort of Zeyu Lu, Guangzhi Zhu, and Guanqun Huang for their discussions on the cascaded image processing approach. This research is supported in part by NIH SBIR grant 1R44AI134500-01 and in part by Johns Hopkins University internal funds and is in collaboration with Sanaria, Inc.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - 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.
AB - 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.
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U2 - 10.1109/COASE.2019.8842953
DO - 10.1109/COASE.2019.8842953
M3 - Conference contribution
AN - SCOPUS:85072986617
T3 - IEEE International Conference on Automation Science and Engineering
SP - 411
EP - 417
BT - 2019 IEEE 15th International Conference on Automation Science and Engineering, CASE 2019
PB - IEEE Computer Society
T2 - 15th IEEE International Conference on Automation Science and Engineering, CASE 2019
Y2 - 22 August 2019 through 26 August 2019
ER -