More than half of croplands in the Midwestern United States are equipped with subsurface drainage pipes to reduce excess water in productive but wet areas. The use of drainage systems not only reduces subsurface water table to prevent waterlogging and flooding but also increases nutrient losses by developing artificial preferential flow paths. The exact locations of subsurface drainage pipes are thus imperative to manage and monitor water quality and nonpoint source pollution. However, such data are not widely available due to private ownership. Previous studies used conventional image filtering methods, thermal images, and ground penetration radar to detect subsurface drainage pipes. Due to surface features, such as furrow and depressions, and their limited data availability, these experiments did not provide a robust approach to identify subsurface drainage pipes over a large area. To overcome these limitations, in this study, we propose a subsurface drainage pipe detection approach based on deep learning with optical images. Our deep learning approach uses a fully convolution network (FCN) architecture that takes an optical image patch as an input and gives an output of pixel-wise drainage pipe detection map. The FCN was trained and validated using optical image datasets obtained from a freeware Google Earth that provides temporally and spatially abundant data. The trained FCN was then applied to large-scale drainage pipe detection tasks to evaluate its performance. The performance comparison between the proposed deep learning approach and conventional image processing techniques (Sobel and Canny edge detection methods) was also carried out. The results demonstrate that the proposed deep learning approach shows accurate and robust drain line detection performance with an average Dice coefficient of 0.58 for validation sets, providing superior performance over the conventional image processing techniques.

美国中西部超过一半的农田都配备了地下排水管，以减少生产性但潮湿的地区的多余水。排水系统的使用不仅减少了地下地下水位以防止涝渍和洪水泛滥，而且还通过开发人为的优先流动路径来增加养分流失。因此，地下排水管的确切位置对于管理和监控水质和面源污染至关重要。但是，由于私有，此类数据无法广泛获得。先前的研究使用常规的图像过滤方法，热图像和地面穿透雷达来检测地下排水管。由于表面特征（例如皱纹和凹陷）及其有限的数据可用性，这些实验没有提供一种可靠的方法来识别大面积的地下排水管。为了克服这些限制，在这项研究中，我们提出了一种基于深度学习的光学图像的地下排水管检测方法。我们的深度学习方法使用完全卷积网络（FCN）架构，该架构以光学图像补丁为输入，并提供像素级排水管检测图的输出。使用从免费的Google Earth（可提供时空上丰富的数据）获得的光学图像数据集对FCN进行训练和验证。然后将训练有素的FCN应用于大型排水管检测任务，以评估其性能。还对建议的深度学习方法与常规图像处理技术（Sobel和Canny边缘检测方法）之间的性能进行了比较。结果表明，所提出的深度学习方法在验证集方面显示出精确而稳健的漏极线检测性能，平均Dice系数为0.58，提供了优于常规图像处理技术的性能。