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A deep feature extractor approach for the recognition of pollen-bearing bees

机译：一种深度特征提取器方法，用于识别花粉蜜蜂

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摘要

In this study, a convolutional neural network (ESA) based feature extracting hybrid model was proposed for the identification of bees carrying pollen or not. The fc6 and fc7 layers of AlexNet and VGG16 which a pre-trained ESA architecture, were used as feature extractors. The performances of the different combinations of the deep properties obtained using the SVM classifier were calculated. The PollenDataset dataset was used to test the proposed model. According to the experimental results, an accuracy score of 97.20% was obtained. As a result, the obtained accuracy score was compared with the state-of-the-art accuracy scores and the proposed model provided better performance than the compared methods.
机译：在该研究中，提出了一种基于卷积神经网络（ESA）提取混合模型的特征，用于鉴定携带花粉的蜜蜂。使用预先训练的ESA架构的AlexNet和VGG16的FC6和FC7层被用作特征提取器。计算使用SVM分类器获得的深度性质的不同组合的性能。 Pollendataset数据集用于测试所提出的模型。根据实验结果，获得了97.20％的精度得分。结果，与最先进的准确度分数进行了比较所获得的精度分数，并且所提出的模型提供比比较方法更好的性能。

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