Automated Activity Identification and Productivity Improvement in Construction using Computer Vision

建筑业经常被认为在自动化运营和提高生产率方面落后于其他行业。造成这一失误的一些挑战是施工所处的崎岖环境和广阔的工地边界。这项研究的目标是创建自动化系统来跟踪建筑活动，并将人工干预降至最低。这将促进应用程序模块的开发，这些模块可以： -确定运营瓶颈，并使用创新的优化例程提出改进建议 -找出效率低下和不必要的空转，以提高盈利能力并减少设备排放 -扩展该技术，以包括夜视和紧急区域的手术 研究中的挑战包括确保这些技术能够承受应用领域(例如建筑和露天采矿)的恶劣环境，以及开发具有弹性的算法，该算法可以在相机移动或识别错误时进行自我纠正。 最终，该系统可用于在极端环境中控制无人装备舰队。

Construction is regularly cited as lagging behind other industries in its effort to automate operations and improve productivity. Some of the challenges that contribute to this lapse are the rugged environment in which construction takes place, and the expanse of the site boundaries. The objective of this research is to create automated systems to track construction activities with a minimum of human intervention. This will facilitate the development of application modules that can: - Identify operational bottlenecks and use innovative optimization routines to suggest improvements - Identify inefficiencies and unnecessary idling to increase profitability and reduce emissions from equipment - Extend the technology to include night vision and operations in emergency zones Challenges in the research include ensuring that the technologies can withstand the the rugged environment of application areas (e.g. construction and open pit mining), and developing resilient algorithms that can self-correct due to camera movement or errors in identification. Ultimately, the system may be used to control unmanned equipment fleets in extreme environments.