## 1 Introduction
Among these, semantic segmentation is most commonly implemented using TensorFlow or PyTorch. TensorFlow is an open-source machine learning framework extensively utilized in deep learning applications. It offers a comprehensive set of tools for constructing, training, and deploying machine learning models, and supports multiple platforms and hardware acceleration. PyTorch, another widely adopted deep learning framework, is characterizedby its dynamic computation graphs, providing greater flexibility and suitability for rapid development and experimentation. For applications such as coal gangue image recognition, PyTorch offers enhanced convenience, and subsequent development and deployment are more easily realized in the field, particularly in the context of coal mining operations. Based on the aforementioned research, with the joint development of computer vision and artificial intelligence, significant technical advancements have been made in gangue identification technology. However, most studies utilize gangue images captured under ideal dust-free conditions, which differ from actual working environments, resulting in limited applicability of the results. To address this issue, this paper employs EfficientNetV2 as the backbone network, improves the DeepLabV3+ model, and introduces an enhanced FPN (Feature Pyramid Network) and ECA (Efficient Channel Attention) mechanism to enhance the accuracy of gangue segmentation. This paper trains a precise segmentation model using dust-free images and subsequently collects gangue images under five different dust concentration gradients in a laboratory setting to simulate various dust conditions. The paper compares the segmentation results of these images before and after denoising in both laboratory and real underground coal mine environments to validate the segmentation accuracy and practicality of the model. The research findings provide valuable guidance for accurately segmenting gangue images in high-dust underground environments. The highlights of this manuscript are as follows:  
1. A similar simulation platform for large-size top-placed coal mining was established, constructed from real coal gangue particle image datasets, and collected images of coal gangue under the influence of different dusts;
2. An improved DeepLabV3+ coal gangue image segmentation model is proposed, which replaces the original DeepLabV3+ backbone network Xception with ResNeSt network, and introduces the FPN and ECA modules to improve the model recognition accuracy. 3. The transmittance estimation defogging method using double dark channel combined with Gaussian weighting is used to study the coal gangue image recognition under the influence of different dust concentrations, which significantly improves the recognition accuracy of the coal gangue under the influence of dust. This manuscript is structured as follows:  
Part I: introduction;  
Part II: coal and gangue image dataset construction;  
Part III: coal gangue image segmentation and defogging methods;  
Part IV: coal gangue image semantic segmentation algorithm;  
Part V: coal gangue image processing and recognition results analysis;  
Part VI: conclusion.