为解决煤矿综采工作面人员尺度多变、危险区域动态变化等因素导致人员入侵危险区域时，视频AI识别准确率不高的问题，提出一种RSCA-YOLOv8s与危险区域自动划分的煤矿综采工作面人员入侵危险区域智能识别方法。针对综采工作面人员识别准确率低问题，在YOLOv8s模型基础上引入RFAConv-SE(Squeeze-and-Excitation with Receptive-Field Attention Convolution)与CCNet(Criss-Cross Attention Network)注意力模块提高复杂背景图像中模型对全局及上下文信息的捕获能力，C2f模块融合Res2Net网络提高模型的多尺度和小目标人员特征提取能力，通过改进的SPC-ASFF(Adaptive Structure Feature Fusion with Sub-Pixel Convolution layer)模块提升模型对多尺度人员特征的自适应融合能力。针对综采工作面摄像头跟随液压支架动态变化导致危险区域在视场范围内动态变化的问题，提出一种基于护帮板、挡煤板标志性目标关键特征点提取的危险区域自动划分方法。针对危险区域不规则变化与基于重叠度的判断方法参数设置困难的问题，提出一种基于射线法判断人员与危险区域像素坐标位置关系的人员入侵危险区域精准识别方法。通过消融试验、RSCA-YOLOv8s与YOLOv5s、YOLOv8-SPDConv等方法对比试验，以及综采工作面7组多场景危险区域自动划分与5组人员入侵危险区域识别试验测试，结果表明：RSCA-YOLOv8s的人员识别方法准确率更高，达到了97.2%，相较基线模型mAP@0.5提高了1.1%，mAP@0.5：0.95提高了2.5%，对小目标人员具有更准确的识别能力和更高的识别精度；该方法危险区域自动划分的平均准确率为97.285%，人员入侵危险区域的判别准确率为98%以上。

To address the problems of low accuracy of video AI recognition of personnel intrusion hazardous areas in fully mechanized mining face caused by factors such as variable personnel scales, and dynamic changes of hazardous areas, an intelligent recognition method for personnel intrusion hazardous areas of fully mechanized mining face based on RSCA-YOLOv8s and automatic division of hazardous areas is proposed. To address the problem of low accuracy of personnel recognition in fully mechanized mining face, the RFAConv-SE (Squeeze-and-Excitation with Receptive-Field Attention Convolution) and CCNet (Criss-Cross Attention Network) attention modules are introduces on the basis of the YOLOv8s model to improve the capture ability of the model for global and contextual information in complex background images. The multi-scale and small target personnel feature extraction ability of the model is improved by fusing the Res2Net network through the C2f module. The adaptive fusion ability of the model for multi-scale personnel features is enhanced through the improved SPC-ASFF (Adaptive Structure Feature Fusion with Sub-Pixel Convolution layer). To address the problem of dynamic changes of hazardous areas within the field of view caused by the dynamic changes of the camera on fully mechanized mining face following the hydraulic support, an automatic division method of hazardous areas based on the extraction of key feature points of landmark targets such as the guard plate and coal baffle plate is proposed. To address the problems of irregular changes of hazardous areas and the difficulty of parameter setting of the judgment method based on overlap degree, a precise recognition method for personnel intrusion hazardous areas based on the ray method to determine the positional relationship between the pixel coordinates of personnel and hazardous areas is proposed. Through ablation experiments, comparison experiments between RSCA-YOLOv8s and methods such as YOLOv5s and YOLOv8-SPDConv, as well as test experiments on automatic division of seven groups of multi-scene hazardous areas and recognition of five groups of personnel intrusion into hazardous areas in fully mechanized mining faces,the results show that the accuracy of the personnel recognition method of RSCA-YOLOv8s is higher, reaching 97.2%, which is 1.1% higher than the baseline model mAP@0.5 and 2.5% higher than mAP@0.5:0.95. It has more accurate recognition ability and higher recognition accuracy for small target personnel. The average accuracy of the automatic division of hazardous areas by this method is 97.285% and the discrimination accuracy of personnel intrusion hazardous areas is more than 98%.