Selective Distillation of Weakly Annotated GTD for Vision-Based Slab Identification System

Abstract

This paper proposes an algorithm for recognizing slab identification numbers in factory scenes. In the development of a deep-learning-based system, manual labeling to make ground-truth data (GTD) is an important but expensive task. Furthermore, the quality of GTD is closely related to the performance of a supervised learning algorithm. To reduce manual work in the labeling process, we generated weakly annotated GTD by marking only character centroids. Whereas bounding-boxes for characters require at least a drag-and-drop operation or two clicks to annotate a character location, the weakly annotated GTD require a single click to record a character location. The main contribution of this paper is on selective distillation to improve the quality of the weakly annotated GTD. Because manual GTD are usually generated by many people, it may contain personal bias or human error. To address this problem, the information in manual GTD is integrated and refined by selective distillation. In the process of selective distillation, a fully convolutional network is trained using the weakly annotated GTD, and its prediction maps are selectively used to revise locations and boundaries of semantic regions of characters in the initial GTD. The modified GTD are used in the main training stage, and post-processing is conducted to retrieve text information. The experiments were thoroughly conducted on actual industry data collected at a steelmaking factory to demonstrate the effectiveness of the proposed method.