Research On Detection And Individual Recognition Of Giant Pandas Based On Convolutional Neural Network

Individual identification is the basis of the wildlife behavior and ecology research.Giant panda(Ailuropoda melanoleuca)is a flagship species of biodiversity protection,how to conduct the long-term monitoring on giant pandas has been a challenge.There are certain limitations of traditional monitoring methods,such as GPS collar method,visual interpretation,etc.,which restricted the large-scale application of these methods in the wild.If a kind of reliable and effective individual identification method of giant pandas can be explored,it will further help the development of relevant researches on giant pandas.Therefore,this paper puts forward a kind of facial identification method of giant pandas based on deep learning techniques,and the main research contents include the following aspects:(1)Make the face image dataset of giant pandas.Collect video data of 48 individual giant pandas from four panda bases by using professional video equipment,among which,the production of dataset including extracting video frames of giant pandas,marking the facial information,sample pretreatment and data augmentation.(2)Detect the facial area of giant pandas.Obtaining the facial area of giant pandas from the images is the first and most important step in the individual recognition method based on the facial image of giant pandas.YOLO v3 object detection algorithm is applied to detect the facial area of giant pandas,and mAP obtained from the model test is 99.54%.The face detection model can not only accurately detect the facial areas of giant pandas of different sizes,but also has a good detection effect on the facial areas of giant pandas with occlusion and different lighting conditions.(3)For the identification difficulties brought by facial perspective and expression changes of giant pandas,a kind of Convolutional Neural Network(CNN)used for the facial identification of giant pandas is given.This model is improved based on VGGNet model by adding Spatial Pyramid Pooling(SPP)for multi-scale pooling,and transferring the features extracted from the shallow layer to the deep layer by cross-layer connection for integration,so as to increase the feature expression ability of the model.At the same time,Batch Normalization(BN)is added to improve the speed and stability of network training.The model can correctly identify about 99% of the giant panda individuals in the validation dataset,and the identification accuracy greatly increases than the original model.It still has strong identification performance even when the facial image is deformed.Finally,the individual giant pandas can be automatically detected and identified through the combination of the detection model and the identification model,and the identification result is conducive to the long-term monitoring and behavioral big data analysis of giant pandas.