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.

钢是一种基本材料，基于视觉的缺陷识别对质量很重要。近年来，深度学习，尤其是卷积神经网络（CNN）成为研究热点。但钢材缺陷类分离性较差，与背景相似，不同的缺陷表现出相似的纹理。这会导致一些无法识别的缺陷，并极大地影响生产。因此，当前的 CNN 仍然需要改进。以此为目标，本文提出了一种具有特征对齐的分层训练-CNN。所提出的方法引入了特征对齐，将无法识别的缺陷映射到可识别的区域，并使用分层训练策略将特征对齐集成到训练过程中。通过这些改进，所提出的方法实现了改进的性能。在公共数据集上的识别结果达到 100%，优于其他 CNN。并成功开发成实际案例，得到显着提升。