Video data of wild animals from infrared cameras always has a large quantity, which takes a lot of work to select and identify. In order to meet the demand of fast automatic identification, this study, using Northeast Tiger and Leopard National Park as an example, is to explore the practicability of using deep learning, convolutional neural networks to automatically identify different animal species, using videos taken by infrared cameras in the wild, under natural conditions. Pictures of each 8 species, captured from the videos from different seasons and of different conditions, consist of the data set. 2074 pictures for train set and 519 for test set. Region of Interest is selected and labeled, the model is YOLO v3 under darknet framework. All pictures are in one data set in the first group of experiment. In the second group, pictures are divided into day(RGB) and night(Grey), and in the third group, divided into day(RGB) and night(Grey) while fine-tuning is used. The mean average precision of our models is from 84.9% to 96.0%, and the models converge. Results show that althoug it is still needed to use better train set to improve the models, using YOLO v3 to identify wild animals automatically is practicable to save manpower and fine-tuning could be an assistance when the train set is small.

为解决大量红外相机监测影像数据量庞大、亟待快速和自动识别的问题，本研究以东北虎豹国家公园为例，应用卷积神经网络，通过深度学习算法对红外相机影像实现物种自动识别。本研究选择8个物种的红外相机视频影像，以50帧率均匀采集成图片格式，每个物种筛选不同角度、不同环境条件的图片，建立图片数据集，训练集2 074张，测试集519张。对图片进行目标打框、信息标注，选用darknet框架下的YOLO v3模型进行训练，首先不区分昼（RGB）夜（灰度）图像进行训练，再区分昼夜进行训练，最后分别对昼夜图像利用微调（fine-tune）进行训练。研究初步结果显示，基于YOLO v3模型对自然条件下拍摄的红外相机图像进行物种自动识别能够一定程度减轻人力负担，但其效果还需通过完善数据集进行提升。fine-tune在小数据集时或可作为辅助。模型对8个物种识别的平均精确率达到

84.9%~96.0%，且模型收敛。