The recognition of steel microstructure images plays a crucial role in the metallographic analysis process. Although some progress has been made through the application of artificial intelligence algorithms, several challenges remain. First, existing algorithms exhibit weak nonlinear feature extraction capabilities and noticeable limitations. Second, they overlook the intrinsic noise and redundant interference present in microscopic images. To address these issues, this paper investigates the automatic recognition of metallographic tissues by leveraging residual structures in deep neural networks. An enhanced residual network model based on transfer learning is proposed, which utilizes the pre-trained weights from the ImageNet dataset to facilitate learning with small sample data. This network offers higher classification accuracy and higher F1 scores. In addition, a deep residual shrinkage network model based on an attention mechanism is proposed. This model incorporates an attention sub-network into the original residual module and employs a soft threshold function to eliminate redundant features, including noise. The proposed algorithms are evaluated against various convolutional neural networks using 20 types of metallographic test sets. The experimental results showed that both methods have high accuracy rates of 95% and 94.44%, respectively, and F1 scores of 0.9464 and 0.9419. While maintaining the complexity of the model, there has been a significant improvement in accuracy, and the models exhibit strong generalization capabilities. Our research contributes to enhancing production efficiency, strengthening quality control, and improving material performance through computer vision technology.

钢材显微组织图像的识别在金相分析过程中起着至关重要的作用。尽管人工智能算法的应用已经取得了一些进展，但仍然存在一些挑战。首先，现有算法表现出较弱的非线性特征提取能力和明显的局限性。其次，他们忽略了显微图像中存在的固有噪声和冗余干扰。为了解决这些问题，本文研究了利用深度神经网络中的残留结构来自动识别金相组织。提出了一种基于迁移学习的增强残差网络模型，该模型利用来自 ImageNet 数据集的预训练权重来促进小样本数据的学习。该网络提供更高的分类精度和更高的 F1 分数。此外，还提出了一种基于注意力机制的深度残差收缩网络模型。该模型将注意力子网络合并到原始残差模块中，并采用软阈值函数来消除冗余特征，包括噪声。使用 20 种金相测试集针对各种卷积神经网络对所提出的算法进行评估。实验结果表明，两种方法的准确率分别为95%和94.44%，F1得分为0.9464和0.9419。在保持模型复杂度的同时，精度有了显着提升，模型表现出较强的泛化能力。我们的研究有助于通过计算机视觉技术提高生产效率、加强质量控制和改善材料性能。