Semi-Supervised Inspection Method of Steel Surface Defects Based on Mean Teacher-BA Model
Surface defects are easy to occur in the process of steel production,and the existing defect detec-tion methods can't achieve accurate detection of steel defects under the premise of providing a few labels.To solve this problem,an improved semi-supervised defect detection method called Mean Teacher-BA is proposed in this paper.Firstly,a bi-level attention module ( BAM),is proposed to increase the model's a-bility to pay attention to and detect complex defects.Secondly,BAM module is applied to the feature ima-ges extracted from backbone network Resnet50 to further improve the feature extraction capability of defect images.Finally,a global adaptive loss strategy is proposed to improve the quality of pseudo-tags generated during training.The experimental results show that the Mean Teacher-BA proposed in this paper is 1.0％,1.2％,1.5％ and 0.9％ higher than the original Mean Teacher model respectively under the conditions of 5％,10％,20％ and 100％.Compared with other supervised and semi-supervised networks,the proposed Mean Teacher-BA is more suitable for deployment and application in industrial production.
deep learningdefect detectionsemi-supervised networkMean Teacher
万方数据
维普
