Two-stage hybrid algorithm for recognition of industrial slab numbers with data quality improvement

Abstract

As the unique recognition of each slab, the accurate recognition of slab number is especially critical for the hot rolling production process. However, the collected data are often of low quality due to poor production environment conditions, making traditional deep learning algorithms face more significant challenges in slab numbers recognition. In this paper, a two-stage hybrid algorithm based on convolutional neural network and Transformer is proposed to identify industrial slab numbers. In the first stage, an improved CycleGAN (HybridCy) is developed to enhance the quality of real-world unpaired data. In the second stage, a multi-scale hybrid vision transformer model (MSHy-Vit) is proposed to identify slab numbers of the improved data output of stage one. The experimental results on industrial slab data show that HybridCy exhibits stable and efficient performance. Even for low-quality data with severe geometric distortion, HybridCy can accomplish quality improvement, which can help to improve recognition accuracy. In addition, the MSHy-Vit achieves superior accuracy in the recognition of slab numbers in comparison to existing methods in the literature.