ABSTRACT Quadcopters equipped with machine learning vision systems are bound to become an essential technique for precision agriculture applications in pastures in the near future. This paper presents a low-cost approach for livestock counting jointly with classification and semantic segmentation which provide the potential of biometrics and welfare monitoring in animals in real time. The method used in the paper adopts the state-of-the-art deep-learning technique known as Mask R-CNN for feature extraction and training in the images captured by quadcopters. Key parameters such as IoU (Intersection over Union) threshold, the quantity of the training data and the effect the proposed system performs on various densities have been evaluated to optimize the model. A real pasture surveillance dataset is used to evaluate the proposed method and experimental results show that our proposed system can accurately classify the livestock with an accuracy of 96% and estimate the number of cattle and sheep to within 92% of the visual ground truth, presenting competitive advantages of the approach feasible for monitoring the livestock.

中文翻译：

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使用 Mask R-CNN 在四轴飞行器航拍图像中进行牲畜分类和计数

摘要 配备机器学习视觉系统的四轴飞行器在不久的将来必将成为牧场精准农业应用的重要技术。本文提出了一种低成本的牲畜计数方法，结合分类和语义分割，提供了实时动物生物识别和福利监测的潜力。论文中使用的方法采用最先进的深度学习技术，称为 Mask R-CNN，对四轴飞行器捕获的图像进行特征提取和训练。已经评估了关键参数，例如 IoU（联合相交）阈值、训练数据的数量以及所提出的系统对各种密度的影响，以优化模型。