论文标题
使用空中图像进行水鸟监测的深度对象检测
Deep object detection for waterbird monitoring using aerial imagery
论文作者
论文摘要
对殖民地水鸟筑巢岛的监测对于跟踪水鸟种群趋势至关重要，水鸟人口趋势用于评估生态系统健康并为保护管理决策提供信息。最近，无人驾驶汽车或无人机已成为一种可行的技术，可以精确监测水鸟殖民地。但是，手动计算数百个或可能数以千计的航空图像的水鸟既困难又耗时。在这项工作中，我们提出了一条深度学习的管道，该管道可用于使用商用无人机收集的空中成像来精确检测，计数和监测水鸟。通过利用基于卷积神经网络的对象探测器，我们表明我们可以检测16种在得克萨斯州沿海殖民筑巢岛上通常发现的水鸟物种。我们使用更快的R-CNN和视网膜对象探测器的实验分别为67.9％和63.1％的平均平均精度得分分别为平均平均精度得分。
Monitoring of colonial waterbird nesting islands is essential to tracking waterbird population trends, which are used for evaluating ecosystem health and informing conservation management decisions. Recently, unmanned aerial vehicles, or drones, have emerged as a viable technology to precisely monitor waterbird colonies. However, manually counting waterbirds from hundreds, or potentially thousands, of aerial images is both difficult and time-consuming. In this work, we present a deep learning pipeline that can be used to precisely detect, count, and monitor waterbirds using aerial imagery collected by a commercial drone. By utilizing convolutional neural network-based object detectors, we show that we can detect 16 classes of waterbird species that are commonly found in colonial nesting islands along the Texas coast. Our experiments using Faster R-CNN and RetinaNet object detectors give mean interpolated average precision scores of 67.9% and 63.1% respectively.
