Yin Yi, Pan Jianwei, Wang Fang, Li Peihang, Cai Zhen, Xu Xin
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430081, P.R. China.
The State Key Laboratory of Refractories and Metallurgy, Wuhan University of Science and Technology, Wuhan, 430081, P.R. China.
Sci Rep. 2025 Jan 24;15(1):3032. doi: 10.1038/s41598-025-87305-z.
Foam ceramics are widely used in industrial applications due to their unique properties, including high porosity, lightweight, and high-temperature resistance. However, their complex microstructure presents significant challenges for image analysis. Traditional machine learning methods often fall short in capturing both global feature dependencies and detailed representations. To address this, a novel artificial intelligence recognition model, FD-Conv, is proposed, which combines the global information processing capabilities of Transformers with the local feature extraction strengths of convolutional neural networks. Additionally, a frequency domain block detail enhancement mechanism is introduced to improve recognition accuracy. Experimental results demonstrate that the FD-Conv model enhances recognition accuracy by at least 7.6% compared to state-of-the-art methods. Furthermore, the model effectively identifies foam ceramics with varying compositions and formulations and quantifies their microstructural phase characteristics. This research aims to advance the application of foam ceramic microstructure image analysis by improving recognition accuracy, particularly in multi-source microscopic image feature learning and pattern recognition.
泡沫陶瓷因其独特性能，包括高孔隙率、轻质和耐高温性，而在工业应用中广泛使用。然而，其复杂的微观结构给图像分析带来了重大挑战。传统机器学习方法在捕捉全局特征依赖性和详细表征方面往往存在不足。为解决这一问题，提出了一种新颖的人工智能识别模型FD-Conv，它将Transformer的全局信息处理能力与卷积神经网络的局部特征提取优势相结合。此外，引入了频域块细节增强机制以提高识别精度。实验结果表明，与现有方法相比，FD-Conv模型将识别精度提高了至少7.6%。此外，该模型能有效识别具有不同成分和配方的泡沫陶瓷，并量化其微观结构相特征。本研究旨在通过提高识别精度，特别是在多源微观图像特征学习和模式识别方面，推动泡沫陶瓷微观结构图像分析的应用。
