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.