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Recognition of Pollen-Bearing Bees from Video Using Convolutional Neural Network

机译:卷积神经网络从视频中识别带花粉的蜜蜂

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In this paper, the recognition of pollen bearing honey bees from videos of the entrance of the hive is presented. This computer vision task is a key component for the automatic monitoring of honeybees in order to obtain large scale data of their foraging behavior and task specialization. Several approaches are considered for this task, including baseline classifiers, shallow Convolutional Neural Networks, and deeper networks from the literature. The experimental comparison is based on a new dataset of images of honeybees that was manually annotated for the presence of pollen. The proposed approach, based on Convolutional Neural Networks is shown to outperform the other approaches in terms of accuracy. Detailed analysis of the results and the influence of the architectural parameters, such as the impact of dedicated color based data augmentation, provide insights into how to apply the approach to the target application.
机译:在本文中,提出了从蜂巢入口的视频中识别带有花粉的蜜蜂的方法。此计算机视觉任务是自动监视蜜蜂的重要组成部分,以便获得有关其觅食行为和任务专长的大规模数据。考虑了用于此任务的几种方法,包括基线分类器,浅层卷积神经网络和文献中的深层网络。实验比较是基于一个新的蜜蜂图像数据集,该数据集已手动注释了花粉的存在。所提出的基于卷积神经网络的方法在准确性方面表现出优于其他方法。对结果的详细分析和架构参数的影响(例如,基于专用颜色的数据增强的影响)提供了有关如何将该方法应用于目标应用程序的见解。

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