论文标题
要在船上发现绵羊
Towards Detection of Sheep Onboard a UAV
论文作者
论文摘要
在这项工作中，我们考虑在80 m高度飞行的无人机（UAV）上检测绵羊的任务。在这个高度，绵羊相对较小，遍布大约15个像素。尽管在过去的十年中，深度学习策略在许多领域都广泛地用于对象检测，但在较小的物体的情况下，最先进的检测器的性能很差。我们开发了一个新颖的绵羊图像数据集，并考虑各种对象探测器，以确定哪个最适合我们的任务，从精度和速度来看。我们的发现表明，使用加权Hausdorff距离作为训练期间的损失功能的UNET检测器是检测无人机绵羊的绝佳选择。
In this work we consider the task of detecting sheep onboard an unmanned aerial vehicle (UAV) flying at an altitude of 80 m. At this height, the sheep are relatively small, only about 15 pixels across. Although deep learning strategies have gained enormous popularity in the last decade and are now extensively used for object detection in many fields, state-of-the-art detectors perform poorly in the case of smaller objects. We develop a novel dataset of UAV imagery of sheep and consider a variety of object detectors to determine which is the most suitable for our task in terms of both accuracy and speed. Our findings indicate that a UNet detector using the weighted Hausdorff distance as a loss function during training is an excellent option for detection of sheep onboard a UAV.
