Wild animal survey using UAS imagery and deep learning: modified Faster R-CNN for kiang detection in Tibetan Plateau

Abstract Wild animal surveys play a critical role in wild animal conservation and ecosystem management. Unmanned aircraft systems (UASs), with advantages in safety, convenience and inexpensiveness, have been increasingly used in wild animal surveys. However, manually reviewing wild animals from thousands of images generated by UASs is tedious and inefficient. To support wild animal detection in UAS images, researchers have developed various automatic and semiautomatic algorithms. Among these algorithms, deep learning techniques achieve outstanding performances in wild animal detection, but have some practical issues (e.g., limited animal pixels and sparse animal samples). Based on a typical deep learning pipeline, faster region based convolutional neural networks (Faster R-CNN), this study adopted several tactics, including feature stride shortening, anchor size optimization, and hard negative class, to overcome the practical issues in wild animal detection in UAS images. In this study, a kiang survey was conducted in UAS datasets (23,748 images) obtained by 14 flight campaigns in the eastern Tibetan Plateau. The validation experiments of our adopted tactics revealed the following: (1) feature stride shortening and anchor size optimization improved small animal detection performance in the animal patch set, increasing the F1 score from 0.84 to 0.86 and from 0.86 to 0.92, respectively; and (2) the hard negative class significantly suppressed false positives in the full UAS image set, increasing the F1 score from 0.44 to 0.86. The test results in the full UAS image set showed that the modified model with the adopted tactics can be applied to either a semiautomatic survey to accelerate manual verification by 25 times or an automatic survey with an F1 score of approximately 0.90. This study demonstrates that the combination of UAS and deep learning techniques can enable automatic/semiautomatic, accurate, inexpensive, and efficient wild animal surveys.