Object detection is a prerequisite task for smart ports to realize behavior recognition of passengers. To address the problems that occlusion caused by crowded queues and gate equipment in fast-track channels leads to decreased detection accuracy of existing models， while also considering the requirements of edge devices， an improved YOLOv12n-based occluded object detection algorithm， MSS-YOLO， is proposed. First， an adaptive multi-scale feature extraction （AMFE） module is designed in the backbone network to replace the downsampling module， enlarge the receptive field of the model， and adaptively weight and focus on key human body feature regions， thereby enhancing multi-scale feature extraction capability and alleviating feature information loss and occlusion interference. Second， an improved feature enhancement module， A2C2f_SEAM， is introduced into the neck network to learn the relationship between occluded and non-occluded regions and dynamically adjust the focus of attention. Finally， CIoU is replaced with Alpha-Shape IoU， and power transformation and geometric constraints are introduced to further optimize bounding box regression performance. Experiments are conducted on a CrowdHuman experimental subset and the self-collected port fast-track channel crossing behavior dataset CBCD. The results show that， compared with the baseline model， the proposed MSS-YOLO improves precision by 1.4 and 6.1 percentage points， recall by 7.1 and 4.3 percentage points， mAP@0.5 by 5.4 and 5.0 percentage points， and mAP@0.5：0.95 by 4.5 and 12.7 percentage points on the two datasets， respectively. Meanwhile， the number of model parameters is reduced by 0.23×10?. MSS-YOLO improves the detection accuracy of occluded persons in port clearance scenarios while maintaining a relatively small parameter scale， providing a foundation for subsequent edge deployment and optimization.