【摘要】 针对煤泥浮选生产过程中因灰分检测依赖传统实验室分析所导致的滞后性问题，提出一种基于多尺度双重注意力的浮选泡沫图像灰分分类预测网络。模型首先基于ResNet101和VGG16构建双分支并行特征提取框架，通过创新性地引入通道注意力机制，设计加权特征融合策略优化多尺度特征整合，并构建顺序串联的双重注意力模块以强化关键特征提取能力。同时，结合空隙度池化模块的局部方差计算，有效提升了纹理特征的表征能力。实验结果表明：该模型在灰分类别预测任务中的准确率达到94.55%,较传统双VGG网络提升31.83%;推理速度达119.54 FPS,较BCNN基准提升52.70%。该方法为浮选过程实时监控提供了有效的技术方案，对提升洗煤厂浮选生产效率和经济效益具有重要实践价值。更多还原

【Abstract】 To address the issue of delayed ash detection in the coal slime flotation process due to reliance on traditional laboratory analysis, Multi-scale Dual Attention coal slime flotation foam image ash content classification prediction network is proposed. The model initially constructs a dual-branch parallel feature extraction framework using ResNet101 and VGG16, with the innovative introduction of channel attention mechanism. A weighted feature fusion strategy is designed to optimize the integration of multi-scale features, and a sequentially connected DoubleAttention module is constructed to enhance the extraction of key features. Simultaneously, the incorporation of a LacunarityPooling module, which calculates local variance, effectively enhances the representation of texture features.Experiments demonstrate that the network model achieves a 94.55% accuracy in predicting ash content classification, improving by 31.83% compared to the traditional dual-VGG branch network. The inference speed of the model reaches 119.54 FPS, improved by 52.70% compared to BCNN. This method provides an effective technical solution for real-time monitoring of the flotation process, offering significant practical value in enhancing the production efficiency and economic benefits of coal washing plants.更多还原