Open AccessSteel Defects Analysis Using CNN (Convolutional Neural Networks)
Author(s) -
Rodion Dmitrievich Gaskarov,
Alexey Mikhailovich Biryukov,
Alexey Fedorovich Nikonov,
Daniil Vladislavovich Agniashvili,
Danil Aydarovich Khayrislamov
Publication year - 2020
Publication title -
èlektronnye biblioteki
Language(s) - English
Resource type - Journals
ISSN - 1562-5419
DOI - 10.26907/1562-5419-2020-23-6-1155-1171
Subject(s) - convolutional neural network , computer science , convolution (computer science) , artificial intelligence , encoder , encoding (memory) , image (mathematics) , process (computing) , artificial neural network , pattern recognition (psychology) , segmentation , deep learning , set (abstract data type) , task (project management) , data set , resolution (logic) , engineering , systems engineering , programming language , operating system
Steel is one of the most important bulk materials these days. It is used almost everywhere - from medicine to industry. Detecting this material's defects is one of the most challenging problems for industries worldwide. This process is also manual and time-consuming. Through this study we tried to automate this process. A convolutional neural network model UNet was used for this task for more accurate segmentation with less training image data set for our model. The essence of this NN (neural network) is in step-by-step convolution of every image (encoding) and then stretching them to initial resolution, consequently getting a mask of an image with various classes on it. The foremost modification is changing an input image's size to 128x800 px resolution (original images in dataset are 256x1600 px) because of GPU memory size's limitation. Secondly, we used ResNet34 CNN (convolutional neural network) as encoder, which was pre-trained on ImageNet1000 dataset with modified output layer - it shows 4 layers instead of 34. After running tests of this model, we obtained 92.7% accuracy using images of hot-rolled steel sheets.