.png){kind=icon}
返回
An imbalanced small sample slab defect recognition method based on image generation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jmapro.2024.03.028.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
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 %.
Keyword:
Data Enhancement
Deep Learning
Defect Recognition
Machine Vision
Neural Networks
Small sample learning
期刊
IF:
6.8
论文数:
8.0K
被引数:
3.5W
机构
引用论文
.png){kind=icon}
Welding defects detection based on deep learning with multiple optical sensors during disk laser welding of thick plates基于多光学传感器深度学习的厚板碟式激光焊接缺陷检测
PREAI
分享收藏An End-to-End Steel Surface Defect Detection Approach via Fusing Multiple Hierarchical Features融合多个层次特征的端到端钢表面缺陷检测方法
OAAI
OAAI分享收藏
查看更多内容