计算机工程与应用2025，Vol.61Issue(10)：214-227,14.DOI:10.3778/j.issn.1002-8331.2401-0188
跨通道细粒度特征融合的矿石图像分类算法
Ore Image Classification Algorithm Based on Cross-Channel Fine-Grained Feature Fusion
摘要
Abstract
In order to solve the problems that the deep learning algorithm has low accuracy in processing ore images with fine-grained texture features,large computing resource requirements and difficulty to deploy on the mobile terminal,a lightweight ore image classification algorithm based on cross-channel fine-grained feature fusion is proposed.A hybrid network is constructed by alternately using CNN and Transformer to effectively extract local and global information of the image.The cross-channel fine-grained feature fusion module is introduced as the feature fuser,and the fusion strategy of channel grouping and random channel shuffle is adopted to enhance the acquisition of ore texture information and main-tain the diversity of fine-grained features.The multi-scale lightweight self-attention module is used to reduce the model parameters,enhance the perception of different scales and spatial locations,and ensure the stability of training and avoid overfitting low-level features.An efficient coordinate attention module is constructed as a fine-grained feature extractor to achieve lightweight and efficient feature extraction.The proposed algorithm achieves 95.78%and 94.77%classification accuracy on the two public ore image datasets of Mineral Photos and Petrology Thin Section Data on the Kaggle platform,respectively.Compared with the other nine lightweight classification networks,such as ShuffleNetV2,MobileNetV3,Reg-Net,ConvNeXtV2,LeViT,EdgeViTs,AFFNeT,EdgeNeXt and MViTV2.The proposed algorithm has fewer parameters(1.27 MB),lower computation(269 MFLOPs)and faster classification speed(219 FPS).关键词
矿石图像分类/卷积神经网络(CNN)/Transformer/跨通道特征融合/注意力机制Key words
ore image classification/convolutional neural network(CNN)/Transformer/cross-channel feature fusion/attention mechanism分类
信息技术与安全科学引用本文复制引用
高云霏,吕伏,冯永安..跨通道细粒度特征融合的矿石图像分类算法[J].计算机工程与应用,2025,61(10):214-227,14.基金项目
国家自然科学基金青年项目(51904144) （51904144）
国家自然科学基金(51874166,51974145) （51874166,51974145）
国家自然科学基金面上项目(52274206) （52274206）
辽宁工程技术大学鄂尔多斯研究院校科技培育合作项目(YJY-XD-2023-014). （YJY-XD-2023-014）
