The Hogweed of Sosnowskyi ( lat. Heracleum sosnówskyi ) is poisonous for humans, dangerous for farming crops, and local ecosystems. This plant is fast-growing and has already spread all over Eurasia: from Germany to the Siberian part of Russia, and its distribution expands year-by-year. In-situ detection of this harmful plant is a tremendous challenge for many countries. Meanwhile, there are no automatic systems for detection and localization of hogweed. In this article, we report on an approach for fast and accurate detection of hogweed. The approach includes the Unmanned Aerial Vehicle (UAV) with an embedded system on board running various Fully Convolutional Neural Networks (FCNN). We propose the optimal architecture of FCNN for the embedded system relying on the trade-off between the detection quality and frame rate. We propose a model that achieves ROC AUC 0.96 in the hogweed segmentation task, which can process 4K frames at 0.46 FPS on NVIDIA Jetson Nano. The developed system can recognize the hogweed on the scale of individual plants and leaves. This system opens up a wide vista for obtaining comprehensive and relevant data about the spreading of harmful plants allowing for the elimination of their expansion.

中文翻译：

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实时检测猪草：深度学习赋能的无人机平台

索斯诺夫斯基的猪草（ 纬度 赫拉克勒姆·索斯诺夫斯基 ) 对人类有毒，对农作物和当地生态系统构成危险。这种植物生长迅速，已经遍布欧亚大陆：从德国到俄罗斯的西伯利亚部分，其分布逐年扩大。这种有害植物的原位检测对许多国家来说是一个巨大的挑战。同时，没有用于检测和定位猪草的自动系统。在本文中，我们报告了一种快速准确检测猪草的方法。该方法包括无人驾驶飞行器 (UAV)，其机载嵌入式系统运行各种全卷积神经网络 (FCNN)。我们根据检测质量和帧速率之间的权衡，为嵌入式系统提出了 FCNN 的最佳架构。我们提出了一个在猪草分割任务中达到 ROC AUC 0.96 的模型，它可以在 NVIDIA Jetson Nano 上以 0.46 FPS 的速度处理 4K 帧。开发的系统可以在单个植物和叶子的尺度上识别猪草。该系统为获取有关有害植物传播的全面和相关数据开辟了广阔的前景，从而消除了它们的扩张。