<i>Panthera unica</i> recognition based on data expansion and ResNeSt with few samples

Zhang Yu; Gao Yayue; Chang Fengyuan; Xie Jiangjian; Zhang Junguo

doi:10.12171/j.1000-1522.20210185

Journal of Beijing Forestry University > 2021 > 43(10): 89-99. > DOI: 10.12171/j.1000-1522.20210185

Zhang Yu, Gao Yayue, Chang Fengyuan, Xie Jiangjian, Zhang Junguo. Panthera unica recognition based on data expansion and ResNeSt with few samples[J]. Journal of Beijing Forestry University, 2021, 43(10): 89-99. DOI: 10.12171/j.1000-1522.20210185

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Panthera unica recognition based on data expansion and ResNeSt with few samples

1.
Qinghai Qilian Mountain Nature Reserve Administration, Qilian County, 810400, Qinghai, China
2.
School of Technology, Beijing Forestry University, Beijing 100083, China

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Received Date: May 13, 2021
Revised Date: June 08, 2021
Available Online: August 03, 2021
Published Date: October 29, 2021

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Abstract

Abstract

Objective The quality of snow leopard monitoring images collected by infrared trigger cameras is uneven and the number is limited. An automatic recognition method of snow leopard monitoring images based on deep learning data expansion was proposed to improve the recognition accuracy of the snow leopard under limited samples.
Method Improving the ResNeSt50 model with attention mechanism, the snow leopard monitoring images of Qilian Mountain National Park of northwestern China were used as the original dataset, the non-snow leopard terrestrial wildlife images taken by the infrared trigger camera were used as the extended negative sample, and the network snow leopard images were used as the extended positive sample. Comparative experiments were conducted in turn based on the above three datasets. The model was gradually guided to focus on the key characteristics of individual snow leopards by choosing an appropriate expansion method, and the effectiveness of the data expansion was verified by Gradient-weighted Class Activation Map.
Result The model trained with the original data set+expanded negative samples+expanded positive samples had the best recognition effect. The Grad-CAM showed that the model correctly focused on the individual pattern and spot characteristics of the snow leopard. Compared with the recognition model based on Vgg16 and ResNet50, ResNeSt50 achieved the best recognition effect, the test set recognition accuracy rate reached 97.70%, the precision rate reached 97.26%, and the recall rate reached 97.59%.
Conclusion The model trained by the original data set+extended negative sample+extended positive sample data expansion method proposed in this paper can distinguish the background from the foreground, and has a strong ability to discriminate the characteristics of snow leopard itself, and the generalization ability is the best.
- Panthera unica,
- monitoring image,
- few sample,
- data expansion,
- convolutional neural network

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References

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Panthera unica recognition based on data expansion and ResNeSt with few samples