Application of Pre-Trained Deep Neural Networks to Identify Cast Billet End Stamp before Heating

This paper is devoted to the efficiency analysis of detectors for the recognition of digits, which are mechanically stamped by a special machine on a steel cast billet. Such detectors are based on pre-trained deep neural networks. In the study, we analyze the performance of four different Faster R-CNN-based detectors. These neural networks have been trained and tested on our own training dataset obtained from the electro-metallurgical plant. According to the experiments, the best results are achieved by the Faster-RCNN Inception-Resnet v2 neural network detector. Its accuracy is about 98% on the test set.