Stagnant zone segmentation with U-net
Abstract-Silo discharging and monitoring the 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 the 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.
- This paper simply proposes to use UNet for the segmentation of stagnant zones in X-ray CTs. While the applicability of this model may represent an advance in the particular field of the authors, the technical contribution of this paper is far from the level expected in this conference. 
- As the paper reads, the main contribution of the paper is the modified version of UNet 'proposed' by the authors, which major modification consists on replacing SGD by Adam. Nevertheless, this cannot be considered a contribution, as changing the optimizer in a deep model is a marginal change, from a methodological point of view. 
- Overall, the quality of the paper is below the standards of ICLR (content, technical contribution, length).
- The submission is not anonymized (authors included their names and affiliations).