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.
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