Adversarial Weakening Based Multi-Stage Classification for Steel Surface Defects
A multi-stage steel surface defect classification algorithm is proposed to address the challenges of small inter-class differences or large intra-class differences in defect features in steel surface defect classifi-cation.The paper first proposes a data preprocessing method based on image splicing,which ensures the quality of defective regions while resizing the image and increases the number of recognizable defective re-gions.A multi-stage classification network is then designed to identify defects based on this preprocessing method,and an adversarial weakening-based defect region mining module is proposed in the network,which makes the two-stage classification network focus more on the defective regions that have not been noticed by the one-stage classification network,so as to compute a more complete defective region.Finally,the classification results of the network are obtained by means of two-stage feature fusion.In addition the algo-rithm utilizes the feature pyramid structure and the channel-space attention mechanism to design an efficient feature extraction network for each stage of classification network to achieve efficient feature extraction.Comparison and ablation experiments are conducted on two steel defect datasets,and the experimental re-sults show that the proposed algorithm outperforms the existing classification models,as well as demon-strates good applicability in real production environment tests.
classification of steel defectsmulti-stage classificationweakness of antagonismfeature pyra-midattention mechanism
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