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

Industrial application using image data collected from actual steelworks.A deep learning based algorithm for localizing slab identification numbers.Deep Convolutional Neural Network (DCNN) for classifying sub-regions.Accumulated confidence for using adjacent outputs of DCNN. 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.