딥 러닝을 이용한 빌렛 번호 인식 알고리즘 (Billet Number Recognition Algorithm using Deep Learning)

서지정보

· 발행기관 : 제어·로봇·시스템학회
· 수록지 정보 : 제어.로봇.시스템학회 논문지 / 23권 / 6호 / 411 ~ 415페이지
· 저자명 : 구교권, 이상준, 김상우

초록

In the steel industry, billet numbers are typically identified before the rolling process because of customers’ different requirements. To identify the billet number, computer vision systems are widely used, because the billet number is marked on the front of the billet by a specialized marking machine at a high temperature. Conventional algorithms, such as rule-based and machine-learning-based algorithms, require features of objects. The features are designed by the user, and they significantly influence the accuracy of the algorithm. To address this problem, deep learning methods have recently been researched. In image processing, the convolutional neural network (CNN) is widely used among the deep learning methods. We propose an end-to-end algorithm using CNN to detect and recognize the billet numbers used in the steel industry. The proposed algorithm consists of four convolutional layers, three pooling layers, two fully connected layers, and a softmax layer. The output of the CNN model consists of the probabilistic values for 18 classes, which include 17 character classes and 1 background class. By using the output of the CNN model, we obtain a character confidence matrix, and by using the score function, the optimal position of the billet number is detected, and the optimal character is classified. Furthermore, we exploit the fact that the billet number consists of four columns and two rows. The experimental results show that the billet number recognition accuracy is maximized as 95.1% in 20 epochs. Using the proposed algorithm will help to increase operation efficiency in the steel industry.

영어초록

In the steel industry, billet numbers are typically identified before the rolling process because of customers’ different requirements. To identify the billet number, computer vision systems are widely used, because the billet number is marked on the front of the billet by a specialized marking machine at a high temperature. Conventional algorithms, such as rule-based and machine-learning-based algorithms, require features of objects. The features are designed by the user, and they significantly influence the accuracy of the algorithm. To address this problem, deep learning methods have recently been researched. In image processing, the convolutional neural network (CNN) is widely used among the deep learning methods. We propose an end-to-end algorithm using CNN to detect and recognize the billet numbers used in the steel industry. The proposed algorithm consists of four convolutional layers, three pooling layers, two fully connected layers, and a softmax layer. The output of the CNN model consists of the probabilistic values for 18 classes, which include 17 character classes and 1 background class. By using the output of the CNN model, we obtain a character confidence matrix, and by using the score function, the optimal position of the billet number is detected, and the optimal character is classified. Furthermore, we exploit the fact that the billet number consists of four columns and two rows. The experimental results show that the billet number recognition accuracy is maximized as 95.1% in 20 epochs. Using the proposed algorithm will help to increase operation efficiency in the steel industry.

참고자료

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