Abstract: 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.