Colchicum autumnale are toxic autumn-blooming flowering plants, which often grow on extensive meadows and pastures. Thus, they pose a threat to farm animals especially in hay and silage. Intensive grassland management or the use of herbicides could reduce these weeds but environment protection requirements often prohibit these measures. For this reason, a non-chemical site- or plant-specific weed control is sought, which aims only at a small area around the C. autumnale and with low impact on the surrounding flora and fauna. For this purpose, however, the exact locations of the plants must be known. In the present paper, a procedure to locate blooming C. autumnale in high-resolution drone images in the visible light range is presented. This approach relies on convolutional neural networks to detect the flower positions. The training data, which is based on hand-labeled images, is further enhanced through image augmentation. The quality of the detection was evaluated in particular for grassland sites which were not included in the training to get an estimate for how well the detector works on previously unseen sites. In this case, 88.6% of the flowers in the test dataset were detected, which makes it suitable, e.g., for applications where the training is performed by the manufacturer of an automatic treatment tool and where the practitioners apply it to their previously unseen grassland sites.

中文翻译：

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使用机器学习方法检测无人机图像中秋水仙碱

秋水仙是有毒的秋季开花植物，通常生长在广阔的草地和牧场上。因此，它们对农场动物构成威胁，尤其是干草和青贮饲料。集约化草地管理或使用除草剂可以减少这些杂草，但环境保护要求往往禁止这些措施。出于这个原因，寻求非化学场地或植物特定杂草控制，其仅针对 C.fallale 周围的小区域，对周围动植物群的影响很小。然而，为此目的，必须知道植物的确切位置。在本论文中，介绍了一种在可见光范围内的高分辨率无人机图像中定位开花的 C.fallale 的程序。这种方法依赖于卷积神经网络来检测花的位置。训练数据，它基于手工标记的图像，通过图像增强得到进一步增强。检测质量特别针对未包含在培训中的草地站点进行评估，以估计检测器在以前看不见的站点上的工作情况。在这种情况下，检测到测试数据集中 88.6% 的花朵，这使其适用于，例如，由自动处理工具的制造商进行培训以及从业人员将其应用于他们以前看不见的草原场地的应用.