## 5 Discussion and Conclusions  
In this paper, an MSS-YOLO network model has been proposed to address the problems of target leakage detection, false detection, and poor real-time performance under limited computational resources, which are faced by miner identification and localization systems in complex environments such as fully mechanized mining faces with high dust concentrations and uneven illumination in underground coal mines. This model efficiently, rapidly, and stably identifies and detects miner targets. Based on the MSS-YOLO detection results, this paper utilized SGBM binocular stereo vision matching technology to achieve real-time localization of miners in three-dimensional space, and experimental validation was carried out by simulating the roadway, which proved the effectiveness of MSS-YOLO. The specific conclusions are as follows:  
1. Aiming to address the problems that traditional deep learning algorithms face, such as being prone to target omission and misdetection under the complex working environment of coal mines and the inability to guarantee good real-time performance under limited computational resources, the MSS-YOLO model, suitable for the identification and detection of miners in a fully mechanized mining face, is proposed. Combining part of the miners' data from the DsLMF+ dataset and miners' data from different distances and different dust concentrations in a simulated roadway environment of a generalized mining face, we constructed the personnel localization dataset UCMD, which contains 7675 images, and manually labeled the miners. Compared with Faster-RCNN, SSD, and other mainstream versions of the YOLO series, the performance of the proposed MSS-YOLO model is significantly improved, and it is able to accomplish the personnel detection and identification tasks in real time and stably under different lighting, different distances, and different cutting dust concentrations. 2. The proposed underground coal mine miner detection network MSS-YOLO was utilized for validation on the UCMD dataset. Compared with the benchmark model YOLOv8n, MSS-YOLO showed improvements of 3.655% on the AP50 metric, 0.494% on AP50-95, 1.336% on Recall, and 1.26% on accuracy and reduced the inference time by 1.2 ms. Although its Rec value was slightly lower than that of YOLOv11n, it outperformed YOLOv11n in all other metrics. 3. In order to realize miner localization in the fully mechanized mining face, this paper combined MSS-YOLO and the SGBM binocular stereo vision matching algorithm. Based on the detection results of MSS-YOLO and the binocular vision disparity solution, the three-dimensional spatial coordinates of the center area of the anchor frame, where the miner was located, were located, and experimental validation of this miner localization method was carried out. The experimental results showed that in the measurement range of 3-10 m in 20 groups, the measurement errors of the positions in the x-, y-, and z-directions were within 0.170 m, 0.160 m, and 0.200 m, with an average error of 0.014 m, 0.008 m, and 0.043 m, respectively. Meanwhile, in terms of the measurement distances, the measurement errors of the distances were within 0.185 m, which meets the maximum permitted positioning error for personnel in the working face of the coal mine. 4. From the above conclusions, it can be seen that the MSS-YOLO model proposed in this paper is capable of obtaining excellent results under the premise of ensuring good real-time performance in recognizing and detecting miners under the complex background of integrated mining faces in coal mines. However, when the vision sensor operates under extreme conditions such as artificial over-obscuration and severe exposure, it can lead to the loss of most of the features of the target miner, resulting in unstable detection results from MSS-YOLO and problems such as missed and false detections.