及时、全面、准确地了解施工现场各种活动的状态和进度，对质量控制、进度跟踪和生产效率分析至关重要，也是全面实现工程精细化管理、智能建造的必要条件。目前，混凝土浇筑仓面施工场景下的进度记录、质量控制仍大多由人工完成，存在及时性不足、误报、漏报等问题。该文将深度学习计算机视觉领域的语义分割和目标检测技术应用到工程建设领域，通过识别模板遮盖比例和吊罐卸料事件获得仓面施工的实时进度，实现秒级精度的坯层覆盖间歇时间超时预警。

Timely, comprehensive and accurate access to the status and progress of various activities on the construction site is essential for quality control, progress tracking and productivity analysis, and is also necessary for the full realization of fine management and intelligent construction. At present, the progress recording and quality control under the concrete pouring construction scenario are still mostly done manually, leading to problems such as insufficient timeliness, misreporting and omission. In this study, the semantic segmentation and object detection technology in the field of deep learning computer vision are applied to the field of engineering construction. Real-time construction progress is obtained by identifying formwork cover ratios and the unloading event of the bucket, and the overtime warning of layer coverage time with second-level accuracy is realized.