施工安全巡检易因视野遮挡导致特征缺失，出现危险误判情况。为提升施工风险识别效率，针对特征遮挡问题，以施工围栏检测为例，提出一种基于amodal补全技术的遮挡特征推理方法。首先，利用图像合成技术建立存在视野遮挡的围栏检测数据集。然后，结合YOLOv8实例分割和ASBU（amodal segmenter based on boundary uncertainty estimation）特征补全网络，推测围栏被遮挡部分的视觉特征。遮挡特征补全后的施工围栏可应用于闭合检测等多种施工安全监管任务。研究选择多个施工场地的围栏图像进行案例验证，可实现精准的围栏缺失特征补全（平均交并比mIoU >95.5%）。研究成果为施工场景的遮挡特征推理提供研究范式，有效提升了智能化施工安全监管效率。

In construction safety inspections, visual obstructions often lead to missing features, resulting in dangerous misjudgments. To improve the efficiency of risk identification in construction, a method for occluded feature inference based on amodal completion technology was proposed, using construction fence detection as a case study. First, a dataset of fence detection images with visual obstructions was created using image synthesis techniques. Then, a combination of YOLOv8 instance segmentation and the amodal segmenter based on boundary uncertainty estimation (ASBU) feature completion network was used to infer the visual features of the occluded parts of the fence. The completed features of the occluded construction fences can be applied to various construction safety monitoring tasks, such as closed-loop detection. The approach was validated using fence images from multiple construction sites, achieving precise feature completion for occluded fences (average intersection ratio mIoU >95.5%). The research results provide a framework for feature inference in occluded construction scenes, which enhances the efficiency of intelligent construction safety supervision.