Species Recognition of Protected Area Based on AutoML
LIU Yao;LUO Ze;Computer Network Information Center, Chinese Academy of Sciences;University of Chinese Academy of Sciences;
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.
【CateGory Index】： S759.9;TP391.41