## 3 Methods
### MSS-YOLO Network Structure
#### 3.1.2 The Shared Convolutional Detection Head Module  
The narrow tunnel, poor lighting conditions, and variable dust concentration in the fully mechanized mining face of the coal mine can lead to poor imaging quality for the images captured by the camera, making it difficult to extract miners' features and increasing the time required for extraction. Furthermore, when the original YOLOv8n model is used for detection in this environment, due to limited underground computing resources, slow inference speed, and other real-time constraints, it cannot meet the needs of real-time monitoring. Therefore, in order to solve the problem of limited computational resources on the working surface and to improve the model inference speed while guaranteeing the detection accuracy, this paper designs an SCDH module to optimize the model on the basis of the original YOLOv8 detection head structure.  
In the SCDH module, Group Normalization (GN) convolution is primarily used to improve the localization and classification performance of the detection head [32]. The structure of the Group Normalization Convolution (Conv_GN) module is shown in Figure 6. In the Conv_GN module, the BN (Batch Normalization) layer in the original CBS module (Figure 3) in the detection head is replaced with a GN layer, which aims to improve the model performance and reduce the complexity by optimizing the consistency of the feature distribution. The traditional BN layer suffers from significant bias in its estimation when the batch size is small, leading to an unstable training process and the introduction of additional noise, which results in significant degradation of the model generalization performance. It is also prone to the problem of insufficient BN when the batch size is too large. In addition, in the inference stage of the model, BN relies on the global statistics accumulated during training, which makes it difficult to adapt to a situation with dynamic input distributions and easily triggers model degradation. GN avoids the network's dependence on the batch dimension and improves the model's generalization ability through the normalization strategy of grouping channels, i.e., calculating the mean and variance of each group in the direction of the channel and normalizing them. Among them, the grouping operation can adapt more flexibly to the distributional characteristics of features at different levels and mitigate the risk of overfitting by reducing the parameter coupling in the normalization process. In this paper, the channel dimensions are divided into 16 groups for independent normalization to eliminate the dependence on batch size and enhance the stability during small batch training, while avoiding the resource overhead of maintaining the global mean and variance in the inference phase.  
At the same time, weight-sharing convolution is constructed by using the same weight parameter. By constructing this weight-sharing convolution to process features at different locations in the input data, the design ensures that only the feature maps at different scales perform convolution operations, rather than using separate convolution layers for each scale. This design can efficiently deal with feature information at different scales, significantly reduce the number of model parameters, reduce the computational overhead of the model, and greatly accelerate the model inference speed, which ensures that the model still has real-time inference in the case of limited computational resources. In addition, in order to cope with problems such as the inconsistent scale of the target features detected by each detector head, this paper uses the Scale layer to scale the features. The structure of the SCDH module is shown in Figure 7.  
Figure 6: The structure of the Conv_GN.