Automatic identification method for terrestrial wildlife in Saihanwula National Nature Reserve in Inner Mongolia of northern China based on ROI-CNN

Liu Wending; Li Anqi; Zhang Junguo; Xie Jiangjian; Bao Weidong

doi:10.13332/j.1000-1522.20180141

Journal of Beijing Forestry University > 2018 > 40(8): 123-131. > DOI: 10.13332/j.1000-1522.20180141

Liu Wending, Li Anqi, Zhang Junguo, Xie Jiangjian, Bao Weidong. Automatic identification method for terrestrial wildlife in Saihanwula National Nature Reserve in Inner Mongolia of northern China based on ROI-CNN[J]. Journal of Beijing Forestry University, 2018, 40(8): 123-131. DOI: 10.13332/j.1000-1522.20180141

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Automatic identification method for terrestrial wildlife in Saihanwula National Nature Reserve in Inner Mongolia of northern China based on ROI-CNN

1.
School of Technology, Beijing Forestry University, Beijing 100083, China
2.
Key Laboratory of State Forestry Administration for Forestry Equipment and Automation, Beijing 100083, China
3.
College of Biological Sciences and Biotechnology, Beijing Forestry University, Beijing 100083, China

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Received Date: April 20, 2018
Revised Date: June 20, 2018
Published Date: July 31, 2018

Graphical Abstract

Abstract

Abstract

ObjectiveThe use of infrared automatic sensing cameras for image surveillance of wildlife is an effective method for the protection and management of wildlife. In order to solve the problem of low accuracy of the automatic identification of wildlife monitoring images caused by the wild complex background environment, a method based on the region of interest (ROI) and convolutional neural network (CNN) was proposed.
MethodImages of five kinds of national protected terrestrial animals such as red deer, goral, pheasant, badger, and wild boar taken in Saihanwula National Nature Reserve, Inner Mongolia of northern China by an infrared automatic sensing camera were used as experimental samples. Using target detection based on regression algorithm, the wild animal images in regional monitoring detection segmentation generated ROI images to overcome the interference of background information on wild animal identification; then perform affine transformations and cropping to achieve random sample data augmentation; proposing a global local VGG16 based on dual channel network to train sample images and use a classifier to output the recognition result finally. At the same time, a dual-channel network model based on VGG19 was constructed to train sample images and compared with the proposed model. The sample images were trained by the algorithm of this paper and VGG16, R-CNN and Fast R-CNN, respectively. The recognition results under different algorithms were compared.
ResultResults showed that the mean average precision (MAP) of the test set was 0.912 compared with the other models and algorithms when sample images were trained by the model in the paper.
ConclusionCompared with other algorithms, the species identification model presented in this paper is more suitable for species identification of wildlife monitoring images under complex backgrounds, and can obtain higher MAP and better recognition effects.
- wildlife,
- monitoring image,
- region of interest,
- convolutional neural network,
- species recognition

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References (24)

References

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Automatic identification method for terrestrial wildlife in Saihanwula National Nature Reserve in Inner Mongolia of northern China based on ROI-CNN