The use of camera traps is a nonintrusive monitoring method to obtain valuable information about the appearance and behavior of wild animals. However, each study generates thousands of pictures and extracting information remains mostly an expensive, time-consuming manual task. Nevertheless, image recognition and analyzing technologies combined with machine learning algorithms, particularly deep learning models, improve and speed up the analysis process. Therefore, we tested the usability of a pre-trained deep learning model available on the TensorFlow hub–FasterRCNN+InceptionResNet V2 network applied to images of ten different European wild mammal species such as wild boar (Sus scrofa), roe deer (Capreolus capreolus), or red fox (Vulpes vulpes) in color as well as black and white infrared images. We found that the detection rate of the correct region of interest (region of the animal) was 94%. The classification accuracy was 71% for the correct species’ name as mammals and 93% for the correct species or higher taxonomic ranks such as “carnivore” as order. In 7% of cases, the classification was incorrect as the wrong species’ name was classified. In this technical note, we have shown the potential of an existing and pre-trained image classification model for wildlife animal detection, classification, and analysis. A specific training of the model on European wild mammal species could further increase the detection and classification accuracy of the models. Analysis of camera trap images could thus become considerably faster, less expensive, and more efficient.

中文翻译：

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使用现有的和预先训练的计算机视觉模型自动检测相机陷阱图像中的欧洲野生哺乳动物物种

使用相机陷阱是一种非侵入式的监视方法，可以获取有关野生动物的外观和行为的有价值的信息。但是，每项研究都会生成数千张图片，而提取信息在很大程度上仍然是一项昂贵，耗时的手动任务。尽管如此，将图像识别和分析技术与机器学习算法（尤其是深度学习模型）相结合，可以改善并加快分析过程。因此，我们测试了可在TensorFlow集线器上使用的预训练深度学习模型的可用性-FasterRCNN + InceptionResNet V2网络，该模型适用于十种欧洲野生哺乳动物物种的图像，例如野猪（Sus scrofa），ro（Capreolus capreolus）或赤狐（Vulpes vulpes）以及黑白红外图像。我们发现正确的感兴趣区域（动物区域）的检测率为94％。对于正确的物种名称（如哺乳动物），分类准确度为71％，对于正确的物种或更高分类等级（如“食肉动物”），其分类准确度为93％。在7％的情况下，由于对错误物种的名称进行了分类，因此分类不正确。在本技术说明中，我们展示了现有的和经过预先训练的图像分类模型在野生动物检测，分类和分析中的潜力。对欧洲野生哺乳动物物种进行模型的特定训练可以进一步提高模型的检测和分类准确性。相机陷阱图像的分析因此可以变得更快，更便宜和更高效。