A Real-Time Multi-Stage Architecture for Pose Estimation of Zebrafish Head with Convolutional Neural Networks
Autor: | Xiang-Xiang He, Qing Shen, Fang-Jun Wang, Zhangjin Huang |
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Rok vydání: | 2021 |
Předmět: |
Artificial neural network
biology Computer science business.industry Detector 020207 software engineering 02 engineering and technology Neurophysiology biology.organism_classification Convolutional neural network Computer Science Applications Theoretical Computer Science Computational Theory and Mathematics Hardware and Architecture Minimum bounding box 0202 electrical engineering electronic engineering information engineering Head (vessel) Computer vision Artificial intelligence business Zebrafish Pose Software |
Zdroj: | Journal of Computer Science and Technology. 36:434-444 |
ISSN: | 1860-4749 1000-9000 |
DOI: | 10.1007/s11390-021-9599-5 |
Popis: | 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. |
Databáze: | OpenAIRE |
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