Multi-Mosquito Object Detection and 2D Pose Estimation for Automation of PfSPZ Malaria Vaccine Production

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.