PDF(Pubmed)
Abstract：
Manual assessment of flower abundance of different flowering plant species in grasslands is a time-consuming process. We present an automated approach to determine the flower abundance in grasslands from drone-based aerial images by using deep learning (Faster R-CNN) object detection approach, which was trained and evaluated on data from five flights at two sites. Our deep learning network was able to identify and classify individual flowers. The novel method allowed generating spatially explicit maps of flower abundance that met or exceeded the accuracy of the manual-count-data extrapolation method while being less labor intensive. The results were very good for some types of flowers, with precision and recall being close to or higher than 90%. Other flowers were detected poorly due to reasons such as lack of enough training data, appearance changes due to phenology, or flowers being too small to be reliably distinguishable on the aerial images. The method was able to give precise estimates of the abundance of many flowering plant species. In the future, the collection of more training data will allow better predictions for the flowers that are not well predicted yet. The developed pipeline can be applied to any sort of aerial object detection problem.
摘要：
人工评估草原上不同开花植物物种的花丰度是一个耗时的过程。我们提出了一种自动方法，通过使用深度学习（FasterR-CNN）对象检测方法，从基于无人机的航拍图像中确定草原上的花朵丰度，对两个地点的五个航班的数据进行了培训和评估。我们的深度学习网络能够识别和分类单个花朵。新方法允许生成满足或超过手动计数数据外推方法精度的花的空间明确图，同时减少了劳动强度。结果对某些类型的花非常好，准确率和召回率接近或高于90%。由于缺乏足够的训练数据等原因，其他花朵的检测效果不佳，由于物候学引起的外观变化，或花朵太小，无法在航拍图像上可靠区分。该方法能够精确估计许多开花植物物种的丰度。在未来,更多的训练数据的收集将允许更好的预测花还没有很好的预测。开发的管道可以应用于任何类型的空中物体检测问题。
