Subsurface tile drainage pipes provide agronomic, economic and environmental benefits. By lowering the water table of wet soils, they improve the aeration of plant roots and ultimately increase the productivity of farmland. They do however also provide an entryway of agrochemicals into subsurface water bodies and increase nutrition loss in soils. For maintenance and infrastructural development, accurate maps of tile drainage pipe locations and drained agricultural land are needed. However, these maps are often outdated or not present. Different remote sensing (RS) image processing techniques have been applied over the years with varying degrees of success to overcome these restrictions. Recent developments in deep learning (DL) techniques improve upon the conventional techniques with machine learning segmentation models. In this study, we introduce two DL-based models: i) improved U-Net architecture; and ii) Visual Transformer-based encoder-decoder in the framework of tile drainage pipe detection. Experimental results confirm the effectiveness of both models in terms of detection accuracy when compared to a basic U-Net architecture.

Deep Learning algorithms consist of a different set of models due to the flexibility that neural network allows while building a full fledged end-to-end model. Advanced architecture can be stated as one that has a demonstrated track record of being an efficient and successful model but the problem arises while dealing with typical tasks related to images. Computer vision is basically based on the theoretical and technological aspect for building artificial systems which have the ability to gather automatic visual information from images or multi-dimensional data. It is focussed on the self-executing extraction, analysis and studying about useful information from a particular image or a sequence of images. Broadly the computer vision consists of tasks like Object Recognition, Identification, Detection, Content-based image retrieval, Image Segmentation and much more. After getting an insight of what basically advanced architecture is and computer vision we move towards the study of some important deep learning advanced architecture.