Abstract
The classification of steel surface defects plays a very important role in analyzing their causes to improve manufacturing process and eliminate defects. However, defective samples are very scarce in actual production, so using very few samples to construct a good classifier is a challenge to be addressed. If the layer number of the model with proper depth is increased, the model accuracy will decrease (not caused by overfit), and the training error as well as the test error will be very high. This is called the degradation problem. In this paper, we propose to use feature extraction + feature transformation + nearest neighbors to classify steel surface defects. In order to solve the degradation problem caused by network deepening, the three feature extraction networks of Residual Net, Mobile Net and Dense Net are designed and analyzed. Experiment results show that in the case of a small sample number, Dense block can better solve the degradation problem caused by network deepening than Residual block. Moreover, if Dense Net is used as the feature extraction network, and the nearest neighbor classification algorithm based on Euclidean metric is used in the new feature space, the defect classification accuracy can reach 92.33% when only five labeled images of each category are used as the training set. This paper is of some guiding significance for surface defect classification when the sample number is small.
Highlights
In the hot rolling manufacturing process of strip steel, defects may occur on its surface due to processing technology, mechanical equipment and human errors
Defective samples are very scarce in the hot rolling process, which poses a challenge to the classification of steel surface defects
In order to verify the influence of different feature extraction networks, feature transformation methods and network depth on the final classification results, the average accuracy of different feature extraction networks were measured through steel surface defect classification experiments
Summary
In the hot rolling manufacturing process of strip steel, defects may occur on its surface due to processing technology, mechanical equipment and human errors. The existing meta-learning methods mostly use an LSTM or Recurrent Neural Network (RNN) structure in the model, but the disadvantages are high time complexity and slow running speed It is not suitable for industrial application. Min Su Kim [1] used a twin neural network based on L1 distance to classify steel surface defect samples, but the performance of the model was not good under small datasets. The model of feature extraction + feature transformation + nearest neighbor was used to classify the steel surface defects in a small dataset. A neural network was used to extract the image features of steel surface defects, after which the extracted image features were transformed to a new feature space and, the nearest neighbor algorithm was used for classification.
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