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

  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.