TY - GEN
T1 - Fast animal detection in UAV images using convolutional neural networks
AU - Kellenberger, Benjamin
AU - Volpi, Michele
AU - Tuia, Devis
PY - 2017/12/1
Y1 - 2017/12/1
N2 - Illegal wildlife poaching poses one severe threat to the environment. Measures to stem poaching have only been with limited success, mainly due to efforts required to keep track of wildlife stock and animal tracking. Recent developments in remote sensing have led to low-cost Unmanned Aerial Vehicles (UAVs), facilitating quick and repeated image acquisitions over vast areas. In parallel, progress in object detection in computer vision yielded unprecedented performance improvements, partially attributable to algorithms like Convolutional Neural Networks (CNNs). We present an object detection method tailored to detect large animals in UAV images. We achieve a substantial increase in precision over a robust state-of-the-art model on a dataset acquired over the Kuzikus wildlife reserve park in Namibia. Furthermore, our model processes data at over 72 images per second, as opposed 3 for the baseline, allowing for real-time applications.
AB - Illegal wildlife poaching poses one severe threat to the environment. Measures to stem poaching have only been with limited success, mainly due to efforts required to keep track of wildlife stock and animal tracking. Recent developments in remote sensing have led to low-cost Unmanned Aerial Vehicles (UAVs), facilitating quick and repeated image acquisitions over vast areas. In parallel, progress in object detection in computer vision yielded unprecedented performance improvements, partially attributable to algorithms like Convolutional Neural Networks (CNNs). We present an object detection method tailored to detect large animals in UAV images. We achieve a substantial increase in precision over a robust state-of-the-art model on a dataset acquired over the Kuzikus wildlife reserve park in Namibia. Furthermore, our model processes data at over 72 images per second, as opposed 3 for the baseline, allowing for real-time applications.
U2 - 10.1109/IGARSS.2017.8127090
DO - 10.1109/IGARSS.2017.8127090
M3 - Conference paper
AN - SCOPUS:85041801567
SN - 9781509049523
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 866
EP - 869
BT - 2017 IEEE International Geoscience and Remote Sensing Symposium
PB - IEEE
CY - Fort Worth
T2 - 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017
Y2 - 23 July 2017 through 28 July 2017
ER -