针对井下危险区域人员监测视频存在光照不均匀、目标尺度不一致、遮挡等复杂情况，基于YOLOv8n网络结构，提出一种改进的井下人员多目标检测算法—YOLOv8n−MSMLAS。该算法对YOLOv8n的Neck层进行改进，添加多尺度空间增强注意力机制（MultiSEAM），以增强对遮挡目标的检测性能；在C2f模块中引入混合局部通道注意力（MLCA）机制，构建C2f−MLCA模块，以融合局部和全局特征信息，提高特征表达能力；在Head层检测头中嵌入自适应空间特征融合（ASFF）模块，以增强对小尺度目标的检测性能。实验结果表明：① 与Faster R−CNN，SSD，RT−DETR，YOLOv5s，YOLOv7等主流模型相比，YOLOv8n−MSMLAS综合性能表现最佳，mAP@0.5和mAP@0.5：0.95分别达到93.4%和60.1%，FPS为80.0帧/s，参数量为5.80×10⁶个，较好平衡了模型的检测精度和复杂度。② YOLOv8n−MSMLAS在光照不均、目标尺度不一致、遮挡等条件下表现出较好的检测性能，适用于现场检测。

This study aims to address the complex challenges in monitoring underground personnel in hazardous areas, including uneven lighting, target scale inconsistency, and occlusion. An innovative multi-target detection algorithm, YOLOv8n-MSMLAS, was proposed based on the YOLOv8n network structure. The algorithm modified the Neck layer by incorporating a Multi-Scale Spatially Enhanced Attention Mechanism (MultiSEAM) to enhance the detection of occluded targets. Furthermore, a Hybrid Local Channel Attention (MLCA) mechanism was introduced into the C2f module to create the C2f-MLCA module, which fused local and global feature information, thereby improving feature representation. An Adaptive Spatial Feature Fusion (ASFF) module was embedded in the Head layer to boost detection performance for small-scale targets. Experimental results demonstrated that YOLOv8n-ASAM outperformed mainstream models such as Faster R-CNN, SSD, RT-DETR, YOLOv5s, and YOLOv7 in terms of overall performance, achieving mAP@0.5 and mAP@0.5: 0.95 of 93.4% and 60.1%, respectively,with a speed of 80.0 frames per second,the parameter is 5.80×10⁶, effectively balancing accuracy and complexity. Moreover, YOLOv8n-ASAM exhibited superior performance under uneven lighting, target scale inconsistency, and occlusion, making it well-suited for real-world applications.