## 1 Introduction
[12] combined the naive Bayes classifier and multilayer perceptron neural network to solve the multiclassification problem of unbalanced samples in high-dimensional mode, and the effect was remarkable. On this basis, [PERSON] et al. [13] proposed a learning method called uncorrelated cost sensitive multiset learning (UCML) to solve the problem of highly unbalanced data classification. This method enhances the robustness of the model by constructing multi-set learning discriminant features and introducing generating adversarial networks to fit the original data distribution. However, relying only on deep learning will lead to overreliance on labeled samples, an approach which cannot be universally applied to all data. Therefore, in recent years the application of multisource information fusion technology in the field of image classification has become more popular. [PERSON] et al. [14] proposed the attention multisource fusion method. In the field of meta-learning, the problem of few-shot learning is solved by means of class alignment, domain attention distribution, and multisource data fusion. [PERSON] et al. [15] used the multiscale information feature fusion method to re-extract the texture information of the bottom layer of the image to focus more attention upon the slender and easily overlooked defects of the strip steel surface, and the detection accuracy of this method reached 98.26%. [PERSON] et al. [16] fused the historical data of strip defects to track model faults, which can assist expert decision-making. After comparison, the method proposed in [4; 5] cannot fully extract the fine-grained information of the image, thus ignoring the local details. Although the feature extraction ability of the model is improved through transfer learning and the attention mechanism, it relies too much on the distribution fitting of the original data [6; 7]. The works of [8; 9; 10; 11] overcome the problem of insufficient defective samples through data enhancement technology, but it cannot cope with the unbalanced data distribution. The method of [13] can combat uneven samples, but it ignores the feature extraction ability of backbone networks and the efficiency and deployment of the models. Therefore, inspired by references [14; 15; 16], this pa per integrates fine-grained information extraction, data sample enhancement, unbalanced sample confrontation, and model generalization performance transfer. To solve the problems of fewer samples and unbalanced data in industrial strip production and meet the requirements of real-time classification on site, a self-adaptive residual shrinkage network classification method based on relational knowledge distillation was proposed. The main contributions are summarized as follows:  
* In the aspect of data enhancement, aiming at the shortage of defect samples in a single production line, a data enhancement method of image mosaic fusion is proposed to improve the matching degree between the model and the data. Aiming at the problem of insufficient defect samples across production lines, an image generation method based on Cycle GAN is proposed to realize the cross domain conversion of defect samples. Compared with the limitations of [8; 9; 10; 11], this method solves the problem of insufficient strip steel defect samples from two angles;
* In terms of feature extraction, based on the integration of the attention mechanism [7], a new backbone network: the self-adaptive residual shrinkage network (SARSN) is proposed to solve the difficulty of image fine-grained feature extraction through a soft threshold and the channel attention mechanism;
* For unbalanced samples, a new adaptive loss function is designed to manage the sample categories separately to achieve a balance between classes to improve the accuracy of the classification model.