Abstract

Wildlife such as deer and other mammals can be detected using drone based aerial imagery and artificial intelligence.

For applications such as population detection, fawn rescue and wildlife damage prevention in disciplines such as ecology, hunting, forestry and agriculture this study was performed in cooperation with the Wildlife Foundation of the Canton of Aargau. We investigated to what extent different methods of automated aerial photography analysis are suitable for wildlife detection using UAV data. In spring 2018, 27 aerial surveys were conducted with fixed-wing UAVs and multicopters over seven game enclosures in North-Western Switzerland and the Southern Black Forest, Germany. Various infrared cameras were used, such as multi-spectral near-infrared sensors (NIR) and thermographic methods (thermal imaging sensors).

The analytical remote-sensing approach showed that in particular thermal images taken at an altitude below 100m AGL are suitable for automation by object recognition algorithms. For this purpose, a deep-learning model (transfer learning using COCO pretrained inception-class Faster R-CNN) was implemented as a modern method of artificial intelligence with Tensorflow and Python. In the training process, property characteristics were extracted from approx. 8000 manually marked animal signatures.

For some species of animals (fallow deer, red deer, bison, goat) extremely robust detection results could be achieved in the subsequent application (inferencing) even in semi-natural mixed forest environments. The efficient, task-specific implementation of the prototype allows real-time analysis of live video feeds under field conditions. With a detection rate of 92.8% per animal, or 88.6% with additional species classification, it was shown that the new technology has tremendous innovation potential for the future of wildlife monitoring.