Abstract
In a complex industrial environment, it is difficult to obtain hot rolled strip steel surface defect images. Moreover, there is a lack of effective identification methods. In response to this, this paper implements accurate classification of strip steel surface defects based on generative adversarial network and attention mechanism. Firstly, a novel WGAN model is proposed to generate new surface defect images from random noises. By expanding the number of samples from 1360 to 3773, the generated images can be further used for training classification algorithm. Secondly, a Multi-SE-ResNet34 model integrating attention mechanism is proposed to identify defects. The accuracy rate on the test set is 99.20%, which is 6.71%, 4.56%, 1.88%, 0.54% and 1.34% higher than AlexNet, VGG16, ShuffleNet v2 1×, ResNet34, and ResNet50, respectively. Finally, a visual comparison of the features extracted by different models using Grad-CAM reveals that the proposed model is more calibrated for feature extraction. Therefore, it can be concluded that the proposed methods provide a significant reference for data augmentation and classification of strip steel surface defects.
Highlights
As one of the main products of the steel industry, hot rolled strip steel is widely used in automobile manufacturing, aerospace and light industry [1]
Our methods provide a reference for solving the small sample and classification problems of strip steel surface defects
Experiments and Results The experiment is based on the following hardware and software environment: Windows10 operating system of Microsoft, Intel(R) Core (TM) i7-11800H CPU, NVIDIA GeForce RTX 3060 Laptop GPU, NVIDIA CUDA-11.1.1 and cuDNN-11.2, Pytorch v1.8.0 deep learning framework
Summary
As one of the main products of the steel industry, hot rolled strip steel is widely used in automobile manufacturing, aerospace and light industry [1]. Bulnes et al [8] proposed a non-invasive system based on computer vision, which uses a neural network for classification and a genetic algorithm to determine the optimal values of the parameters This method improves flexibility and the whole process can be executed quickly. Liu et al [14] proposed a classification method based on deep CNN, adding an identity mapping to GoogLeNet and using this network to detect defects (such as scar, burrs, inclusion) with an accuracy of 98.57%. The deep learning-based classification algorithms for strip steel surface defects has been effective, but there are still shortcomings in the current research. Based on Generative Adversarial Network(GAN) and attention mechanism, accurate classification of strip steel surface defects is realized. The loss functions of generator and discriminator are defined as LossG and LossD, respectively, as shown in Equations (3) and (4)
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