A Real-Time Multi-Stage Architecture for Pose Estimation of Zebrafish Head with Convolutional Neural Networks
In order to conduct optical neurophysiology experiments on a freely swimming zebrafish, it is essential to quantify the zebrafish head to determine exact lighting positions. To efficiently quantify a zebrafish head's behaviors with limited resources, we propose a real-time multi-stage architect...
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Published in | Journal of computer science and technology Vol. 36; no. 2; pp. 434 - 444 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Singapore
Springer Singapore
01.04.2021
Springer Springer Nature B.V School of Computer Science and Technology,University of Science and Technology of China,Hefei 230027,China School of Data Science,University of Science and Technology of China,Hefei 230027,China Anhui Province Key Laboratory of Software in Computing and Communication,Hefei 230027,China%School of Computer Science and Technology,University of Science and Technology of China,Hefei 230027,China%School of Computer Science and Technology,University of Science and Technology of China,Hefei 230027,China |
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Online Access | Get full text |
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Summary: | In order to conduct optical neurophysiology experiments on a freely swimming zebrafish, it is essential to quantify the zebrafish head to determine exact lighting positions. To efficiently quantify a zebrafish head's behaviors with limited resources, we propose a real-time multi-stage architecture based on convolutional neural networks for pose estimation of the zebrafish head on CPUs. Each stage is implemented with a small neural network. Specifically, a light-weight object detector named Micro-YOLO is used to detect a coarse region of the zebrafish head in the first stage. In the second stage, a tiny bounding box refinement network is devised to produce a high-quality bounding box around the zebrafish head. Finally, a small pose estimation network named tiny-hourglass is designed to detect keypoints in the zebrafish head. The experimental results show that using Micro-YOLO combined with RegressNet to predict the zebrafish head region is not only more accurate but also much faster than Faster R-CNN which is the representative of two-stage detectors. Compared with DeepLabCut, a state-of-the-art method to estimate poses for user-defined body parts, our multi-stage architecture can achieve a higher accuracy, and runs 19x faster than it on CPUs. |
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ISSN: | 1000-9000 1860-4749 |
DOI: | 10.1007/s11390-021-9599-5 |