21st EANN 2020, 5 -7 June 2020, Greece

Semantic Segmentation Based on Convolution Neural Network for Steel Strip Position Estimation

Aline de Faria Lemos, Bal√°zs Vince Nagy

Abstract:

  In this paper, a method to access the location of a steel strip in the rolling process was developed. The method consists of a hybrid system composed of a CNN-based semantic segmentation followed by morphological operation and outlier removal. The proposed method was capable of estimating the position of the strip with high precision and low computational burden, making it suitable for the application. The implementation of automatic estimation for the steel strip positioning, replacing the current human operation, can yield substantial costs saving. Future work will be carried out for the and integration of automatic control in the process.  

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