## 3 Methods
### MSS-YOLO Network Structure
#### 3.1.1 The Multi-Scale Edge Enhance Module  
Due to the high concentration of dust diffusion and uneven illumination in the environment of the fully mechanized mining face in the coal mine, the target features in images are not obvious. As a result, the traditional YOLOv8 detection algorithm struggles to extract miners' features and is prone to losing target features, leading to omissions, false detections, etc. In order to ensure the stability of miner detection and improve the accuracy of the object detection model in the miner recognition task in the integrated mining face of coal mines, this paper designs an MSEE module. This module is combined with the C2f module to enhance the model's ability to recognize miner targets of different scales under high dust concentrations and to enhance the expression of edge information.  
In general, image edge information usually appears in regions with more drastic pixel variations, i.e., locations with large pixel gradient changes. The average pooling operation can smooth the input feature map by applying low-pass filtering to separate high-frequency features. Then, the edge features are obtained by subtracting the low-pass filtered feature map from the original feature map. The calculation formula is shown in Equation (1):  
\[E_{edge}=X-AvgPool(X) \tag{1}\]  
where \(X\) is the input feature map, \(AvgPool(X)\) is the result after average pooling of \(X\), and \(E_{edge}\) is the edge feature map. Based on the above theory, in the MSEE module, this paper uses a \(3\times 3\) average pooling layer to smooth the input features to extract the low-frequency  
Figure 3: The architecture of the MSS-YOLO network model.  
information of the feature map. Then, the original feature map is used to subtract the pooled low-frequency information to obtain the edge information, \(X\). Then, a convolutional layer is used to learn the edge information weights, and the output of the extracted edge feature information is weighted by the sigmoid activation function, and the final edge feature weights are calculated as shown in Equation (2):  
\[E_{w}=\sigma\left(W*E_{edge}+b\right) \tag{2}\]  
where \(E_{w}\) is the edge feature weight obtained after the sigmoid operation; \(\sigma\) is the sigmoid activation function, whose output range is [0, 1]; \(W\) is the convolution operation; and \(b\) is the bias term. Then, it is superimposed with the input feature map in the form of residual concatenation to obtain the feature map after enhancing the edge information, which improves the ability of the network to perceive the target edge. The feature Edge Enhancement (EE) module is shown in Figure 4.  
In order to further improve the object detection performance of fuzzy-feature images under harsh conditions, this paper designs four groups of feature branch extraction structures with different scales of 3, 6, 9, and 12 in parallel, based on the pooling layer of AdaptiveAvgPool. This allows the network to capture detailed information at different scales. After applying convolution to the feature maps at different scales, the EE module is used to enhance the edge contour features and details of the target in the image, and then the multi-scale feature maps are spliced together. This compensates for the insufficient extraction of different scales of the edge contour of the network for the coal mine underground and effectively improves the network's performance in the detection of the weak texture and weak edge targets caused by dense fog and vibration. The structure of the MSEE module is shown in Figure 5.  
Figure 4: The feature Edge Enhancement (EE) module.  
Figure 5: The structure of the MSEE module.