卷积神经网络
计算机科学
人工智能
模式识别(心理学)
特征(语言学)
人工神经网络
新认知
深度学习
特征提取
时滞神经网络
语言学
哲学
作者
Yiping Gao,Liang Gao,Xinyu Li
标识
DOI:10.1016/j.rcim.2022.102507
摘要
Steel is a basic material, and vision-based defect recognition is important for quality. Recently, deep learning, especially convolutional neural network (CNN), has become a research hotspot. However, steel defects have poor class separation, which is similar to the background, and different defects show similar textures. This causes some defects unrecognizable and influences production greatly. Thus, current CNNs still need to be improved. With this goal, this paper proposes a hierarchical training-CNN with feature alignment. The proposed method introduces a feature alignment, which maps the unrecognizable defects to the recognizable area, and a hierarchical training strategy is used to integrate the feature alignment into the training process. With these improvements, the proposed method achieves improved performance. The recognition results on a public dataset achieve 100%, which outperforms the other CNNs. And it has been developed into a real-world case successfully, which is significantly improved.
科研通智能强力驱动
Strongly Powered by AbleSci AI