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    • 摘要: 针对焦炭光学组织图像存在的多成分混叠而导致的分割边界不清问题,提出了一种MD-UNet语义分割模型。该模型以VGG16为主干网络,并在编码器最深层处引入CloAttention注意力模块,通过上下文感知的局部增强和全局注意力机制,使得模型能够更好地聚焦于图像中关键区域,增强对焦炭光学组织复杂纹理的感知能力。然后,设计了多分支扩张融合模块(multi-branch dilated fusion, MBDF)代替传统解码器的卷积模块,以充分保留并融合不同尺度的信息,增强特征表达能力,避免信息丢失和细节模糊。最后,采用GELU激活函数替代ReLU激活函数,以处理网络训练中出现的梯度消失问题。在语义分割模型的对比实验中,MD-UNet模型对焦炭光学组织的分割效果最佳,该模型在mIoU和F1-Score指标上分别达到了88.72%和94.28%,显著优于传统语义分割模型,充分验证了其在提高焦炭光学组织分割精度方面的有效性。

       

      Abstract: Addressing the challenge of unclear segmentation boundaries arising from multi-component aliasing in coke optical tissue images, this paper proposes a MD-UNet semantic segmentation model. This model employs VGG16 as its backbone network and incorporates the CloAttention module at the deepest level of the encoder. By leveraging context-aware local enhancement and a global attention mechanism, CloAttention enables the model to focus better on critical image regions and enhances the perception of the complex textures inherent in coke optical tissues. Furthermore, a multi-branch dilated fusion (MBDF) module has been designed to replace the conventional convolution modules in the decoder. This substitution aims to effectively preserve and integrate multi-scale information, thereby enriching feature representation and mitigating information loss and detail blurring. Finally, the GELU activation function is adopted in place of ReLU to address the vanishing gradient problem encountered during network training. Comparative experiments on semantic segmentation models demonstrate that the proposed MD-UNet model achieves the most superior segmentation performance on coke optical tissues, reaching mIoU and F1-Score values of 88.72% and 94.28%, respectively. These results significantly outperform traditional semantic segmentation models, thereby validating the effectiveness of MD-UNet in enhancing the segmentation accuracy of coke optical tissues.