用注意力增强的ConvNeXt模型提升岩石颗粒分类精度。
Deep Learning-Based Rock Particulate Classification Using Attention-Enhanced ConvNeXt
- 在ConvNeXt基础上加入自注意力与通道注意力机制。
- 在岩石图像数据集上分类准确率显著优于三个基线模型。
- 适合需要精细纹理分类的地质与采矿工程应用。
岩石尺寸的精确分类是岩土工程、采矿和资源管理中的关键环节，直接影响运营效率与安全。本文提出一种基于ConvNeXt架构的增强型深度学习模型，融合自注意力与通道注意力机制。该模型（CNSCA）通过自注意力捕捉长距离空间依赖，通过通道注意力强化重要特征通道，从而有效兼顾岩石图像中的细粒度局部模式与全局上下文关系，提升分类精度与鲁棒性。我们在一个岩石尺寸分类数据集上评估模型，并与三个强基线进行对比。结果表明，注意力机制的引入显著提升了模型在自然纹理（如岩石）细粒度分类任务中的性能。
原文摘要 · Abstract (English)
Accurate classification of rock sizes is a vital component in geotechnical engineering, mining, and resource management, where precise estimation influences operational efficiency and safety. In this paper, we propose an enhanced deep learning model based on the ConvNeXt architecture, augmented with both self-attention and channel attention mechanisms. Building upon the foundation of ConvNext, our proposed model, termed CNSCA, introduces self-attention to capture long-range spatial dependencies and channel attention to emphasize informative feature channels. This hybrid design enables the model to effectively capture both fine-grained local patterns and broader contextual relationships within rock imagery, leading to improved classification accuracy and robustness. We evaluate our model on a rock size classification dataset and compare it against three strong baseline. The results demonstrate that the incorporation of attention mechanisms significantly enhances the models capability for fine-grained classification tasks involving natural textures like rocks.
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