基于AutoML的保护区物种识别
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Species Recognition of Protected Area Based on AutoML
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    摘要:

    随着我国在生态保护上的投入加大,红外相机技术在我国各级自然保护区的应用发展迅猛,在如何充分挖掘照片的信息方面,物种识别显得尤为重要,是其他工作的前提.在图像识别方面,随着深度学习的爆发,给图像识别带来了革命性的提升,以卷积神经网络为代表的网络结构在准确率上几乎完胜传统方法.然而,由于网络结构对最终图像识别准确率的影响巨大,人们在实际应用中往往都是使用一些经典的网络结构,比如VGG16、VGG19、ResNet50等,从中选择一个适合自己的数据集的网络结构,同时对于不同的数据集,可能需要重新选择.因此,在保护区红外相机物种的识别中,本文提出了基于AutoML的自动构建网络结构技术,针对不同的保护区的数据集,自动构建合适的网络结构,避免人工选择,同时达到了与人工选择网络相当的准确率.

    Abstract:

    With the increase of investment in ecological protection, the application of infrared camera technology in natural reserves has developed rapidly. Species recognition, which is particularly important in how to fully mine photo information, is the premise of other work. In image recognition, with the outbreak of deep learning, the image recognition has been revolutionized. Convolutional neural network as the representative network structure almost completely overcomes the traditional method in accuracy. However, due to the huge impact of the network structure on the accuracy of the final image recognition, people often choose a network structure suitable for their own dataset from some classic network structures, such as VGG16, VGG19, ResNet50, and so on, in practical applications. Nevertheless, it may need to re-select network structure for different datasets. Therefore, in the species recognition of protected area, this study proposes an automatic construction network structure technology based on AutoML. The technology can automatically build appropriate network structures for different datasets of protected area to avoid manual selection of network structures. At the same time, the technology achieves an accuracy comparable to manual selection of network structures.

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刘耀,罗泽.基于AutoML的保护区物种识别.计算机系统应用,2019,28(9):147-153

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  • 收稿日期:2019-02-27
  • 最后修改日期:2019-03-15
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  • 在线发布日期: 2019-09-09
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