Combining Self-attention and Dilation Convolutional for Semantic Segmentation of Coal Maceral Groups
Journal:
arXiv
Published Date:
Jun 15, 2025
Abstract
The segmentation of coal maceral groups can be described as a semantic
segmentation process of coal maceral group images, which is of great
significance for studying the chemical properties of coal. Generally, existing
semantic segmentation models of coal maceral groups use the method of stacking
parameters to achieve higher accuracy. It leads to increased computational
requirements and impacts model training efficiency. At the same time, due to
the professionalism and diversity of coal maceral group images sampling,
obtaining the number of samples for model training requires a long time and
professional personnel operation. To address these issues, We have innovatively
developed an IoT-based DA-VIT parallel network model. By utilizing this model,
we can continuously broaden the dataset through IoT and achieving sustained
improvement in the accuracy of coal maceral groups segmentation. Besides, we
decouple the parallel network from the backbone network to ensure the normal
using of the backbone network during model data updates. Secondly, DCSA
mechanism of DA-VIT is introduced to enhance the local feature information of
coal microscopic images. This DCSA can decompose the large kernels of
convolutional attention into multiple scales and reduce 81.18% of
parameters.Finally, we performed the contrast experiment and ablation
experiment between DA-VIT and state-of-the-art methods at lots of evaluation
metrics. Experimental results show that DA-VIT-Base achieves 92.14% pixel
accuracy and 63.18% mIoU. Params and FLOPs of DA-VIT-Tiny are 4.95M and 8.99G,
respectively. All of the evaluation metrics of the proposed DA-VIT are better
than other state-of-the-art methods.
