Stagnant Zone Segmentation with U-Net

Silo discharging and monitoring process for industrial or research application depend on computerized segmentation of different parts of images such as stagnant and flowing zones which is the toughest task. X-ray Computed Tomography (CT) is one of a powerful non-destructive technique for cross-sectional images of a 3D object based on X-ray absorption. CT is the most proficient for investigating different granular flow phenomena and segmentation of stagnant zone as compared to other imaging techniques. In any case, manual segmentation is tiresome and erroneous for further investigations. Hence, automatic and precise strategies are required. In the present work, a U-net architecture is used for segmenting the stagnant zone during silo discharging process. This proposed image segmentation method provides fast and effective outcomes by exploiting a convolutional neural networks technique with an accuracy of 97 percent.