Research On Intelligent Monitoring For Interior Visual Construction Progress

Tracking and monitoring of construction progress are vital for achieving project schedule and cost objectives.However,indoor progress tracking poses greater complexities compared to outdoor projects,primarily due to the involvement of numerous construction participants and the intricate interconnection of construction flow operations.The accurate and effective collection of on-site construction information,along with its timely retrieval and analysis,remains a pressing challenge in indoor progress tracking.The existing methods for indoor construction information collection still rely on traditional manual inspection and recording,resulting in low efficiency in progress information collection,processing,and analysis.Consequently,it difficult to meet the needs of effectively transmitting and managing complex indoor construction progress information.To address this issue,this thesis proposes an intelligent tracking method that based on dynamic information feedback theory for indoor construction virtual progress.The main research contents are as follows:(1)Construction of an intelligent tracking framework for indoor construction visual progress.Combining with the dynamic information feedback theory,the connotation and significance of informatization management of indoor visual progress are analyzed,and it is clarified that information collection,recognition,and analysis are important supporting technologies for dynamic information management.Based on the construction progress information composition and management process at different stages,the characteristics of construction progress information and the necessity of informatization progress management are elaborated.Considering the practical needs of indoor construction visual progress tracking,the challenges and difficulties of key technologies in existing visual progress informatization management are analyzed,and a method for dynamic,real-time tracking and monitoring of indoor construction visual progress based on information collection,recognition,and analysis is proposed.(2)Method for indoor construction visual progress information collection.This study examines the applicability and limitations of existing computer vision-based 3D reconstruction methods,specifically focusing on the challenges of indoor construction environment with weak textures.These challenges include difficulties in feature extraction,data redundancy,and interference from unfavorable lighting conditions,which hinder the efficient and accurate acquisition of indoor construction progress information.To address this issue,a framework for collecting visual progress information in indoor textureless construction environments is developed.For small-scale indoor materials,a hierarchical optimization-based method using depth cameras is proposed for rapid 3D reconstruction.For large-scale secondary structures in indoor environments,a visual-guided approach utilizing mobile devices is introduced for rapid 3D reconstruction.This approach enables efficient and accurate collection of indoor visual progress information in textureless construction environments and provides a solid foundation for intelligent tracking of indoor construction visual progress.(3)Method for indoor construction visual progress information recognition.Focusing on the requirements of key elements in indoor construction progress,leveraging the advantages of deep learning in construction scene image classification and recognition.It tackles challenges such as mutual occlusion,high texture similarity,and difficult edge recognition in indoor construction scenes.A point cloud-based semantic segmentation and recognition model is proposed for tracking elements of indoor visual progress.Building upon the Rand LA-Net model,a refined and fused segmentation model called RF-Rand LANet is proposed.Compared to the former,the Rand LA-Net method significantly improves indicator of mean accuracy and mean intersection over union.As a result,the proposed method enables the recognition of construction visual progress elements from point clouds,providing a foundation for subsequent dynamic construction progress tracking and analysis based on indoor as-built point cloud.(4)Method for indoor visual progress information analysis.Considering the high sensitivity of indoor installation engineering changes and progress,a dynamic analysis and tracking framework for construction progress information based on indoor real-scene point cloud is constructed.To identify the point cloud differences of construction progress tracking elements obtained in interval time,an automatic registration method based on point cloud patches’ features representions is proposed,which improves the indicator of the recall rate and precision rate compared to the FPFH algorithm.Finally,based on the information collection,identification,and analysis of construction progress,an intelligent progress tracking and notification system prototype is designed and development.This thesis focuses on the application of computer vision and artificial intelligence technology in the management of indoor construction progress.It aims to achieve the tracking and monitoring of indoor progress at the construction process level and provides new ideas for lean construction progress management.