论文标题
迈向内场导航：利用模拟数据进行裁切行检测
Towards Infield Navigation: leveraging simulated data for crop row detection
论文作者
论文摘要
农作物行检测的农业数据集通常受其图像数量有限的约束。这限制了研究人员开发基于深度学习的模型，用于涉及农作物行检测的精确农业任务。我们建议利用小型现实世界数据集以及模拟生成的其他数据，以产生类似的作物行检测性能，与使用大型现实世界数据集训练的模型相似。我们的方法可以通过使用少60％的现实世界数据来实现基于深度学习的作物行检测模型的性能。我们的模型在诸如阴影，阳光和生长阶段之类的野外变化方面表现良好。我们引入了一条自动管道，以生成模拟域中的作物行检测的标记图像。进行了广泛的比较，以分析模拟数据在各种现实世界场景中达到可靠的作物行检测的贡献。
Agricultural datasets for crop row detection are often bound by their limited number of images. This restricts the researchers from developing deep learning based models for precision agricultural tasks involving crop row detection. We suggest the utilization of small real-world datasets along with additional data generated by simulations to yield similar crop row detection performance as that of a model trained with a large real world dataset. Our method could reach the performance of a deep learning based crop row detection model trained with real-world data by using 60% less labelled real-world data. Our model performed well against field variations such as shadows, sunlight and grow stages. We introduce an automated pipeline to generate labelled images for crop row detection in simulation domain. An extensive comparison is done to analyze the contribution of simulated data towards reaching robust crop row detection in various real-world field scenarios.
