Offsite construction is an approach focused on moving construction tasks from traditional jobsites to manufacturing facilities. Improved productivity of construction tasks is paramount in terms of competitiveness and is achieved through the continuous improvement of operations and planning, which often relies on historical data obtained from previous projects. Despite being a common practice, current methods, such as time studies, are not able to capture the changing scenarios resulting from improvements to production. This paper presents a novel approach to automatically detect and track the progress of construction operations by applying a method that combines deep learning algorithms and finite state machines to existing footage captured by closed-circuit television (CCTV) security cameras. Applied in the context of floor panel manufacturing stations, the proposed method examines entire production days recorded by CCTV cameras, while providing the durations of each task, its required resources, and the task efficiency per panel with high accuracy.

异地施工是一种将施工任务从传统工地转移到制造设施的方法。就竞争能力而言，提高施工任务的生产率至为重要，这是通过不断改进运营和规划来实现的，而运营和规划的改进常常依赖于从先前项目获得的历史数据。尽管是一种惯例，但是诸如时间研究之类的当前方法仍无法捕获由于生产改进而导致的不断变化的情况。本文提出了一种新颖的方法，该方法通过将结合深度学习算法和有限状态机的方法应用于闭路电视（CCTV）安全摄像机捕获的现有镜头，来自动检测和跟踪施工作业的进度。