In order to accurately count the number of animals grazing on grassland, we present a livestock detection algorithm using modified versions of U-net and Google Inception-v4 net. This method works well to detect dense and touching instances. We also introduce a dataset for livestock detection in aerial images, consisting of 89 aerial images collected by quadcopter. Each image has resolution of about 3000×4000 pixels, and contains livestock with varying shapes, scales, and orientations.We evaluate our method by comparison against Faster RCNN and Yolo-v3 algorithms using our aerial livestock dataset. The average precision of our method is better than Yolo-v3 and is comparable to Faster RCNN.

中文翻译：

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使用完全卷积网络的航空影像中的牲畜检测

为了准确计算在草原上放牧的动物数量，我们提出了一种使用U-net和Google Inception-v4网络的改进版本的牲畜检测算法。此方法很好地检测密集且动人的实例。我们还引入了航空图像中用于牲畜检测的数据集，该数据集由四轴飞行器收集的89个航空图像组成。每个图像的分辨率约为3000×4000像素，并包含形状，比例和方向各异的牲畜。我们通过使用航空牲畜数据集与Faster RCNN和Yolo-v3算法进行比较来评估我们的方法。我们方法的平均精度优于Yolo-v3，可与Faster RCNN相提并论。