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An imbalanced small sample slab defect recognition method based on image generation
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AI
DOI：10.1016/j.jmapro.2024.03.028.png)
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Abstract
En 中文
The surface quality of slabs has a profound impact on the subsequent performance and quality of steel. Accurate detection and classification of surface defects are crucial for improving steel quality. However, online visual inspection in the steel industry faces challenges such as sparse data, unclear features, sample imbalances, and impeding the application of deep learning in slab defect detection. To address these challenges, this paper introduces a novel approach utilizing the Slab Defect Generation (SDG) network to generate additional images by training on imbalanced small samples of slab defects, thereby constructing a comprehensive defect database. This methodology significantly enhances the efficiency and accuracy of deep learning in slab defect detection. Furthermore, an improved loss function is proposed specifically for the You Only Look Once version 5 (YOLOv5) model to augment the training of challenging samples. Experimental validation confirms the superiority of our method in terms of both image generation quality and defect -recognition compared to State -Of -The -Art (SOTA) models. The accuracy achieved by our approach is 98.6 %, outperforming the second -ranking model with an accuracy of 90.1 %.
Keywords:
Data Enhancement
Deep Learning
Defect Recognition
Machine Vision
Neural Networks
Small sample learning
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6.8
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