Localization of the slab information in factory scenes using deep convolutional neural networks

This paper proposes a novel algorithm for localizing slab identification numbers (SINs) in factory scenes. Automatic identification of product information is important for the process management, and localization of SINs in complex scenes is a major challenge for the recognition. A previous rule-based localization algorithm for SINs requires lots of prior knowledge and heuristic tuning for parameters. In this paper, a deep convolutional neural network (DCNN) is employed to overcome these limitations, and accumulated confidence is proposed to utilize neighboring outputs of the DCNN in a scene. The localization error is remarkably reduced to 1.44% by the proposed algorithm compared to 4.59% in the previous work. The proposed data-driven method can be applied to construct other automatic identification systems with minimal manual handling.