Road Construction Progress Monitoring Video Dataset

The current construction industry faces significant challenges in monitoring construction progress and quality. Traditional methods primarily rely on manual inspection and recording, which are inefficient and prone to errors. Existing video monitoring methods are typically used only for simple video archiving and lack intelligent analysis capabilities, making real-time and accurate project progress monitoring difficult. This dataset aims to provide clear and continuous video of construction sites to assist in developing intelligent monitoring and analysis systems, thereby improving construction efficiency and quality management.The data is collected using high-definition cameras installed at construction sites, ensuring coverage of main construction areas. Video data is annotated through multiple rounds of manual labeling, including construction phases, equipment usage, personnel activities, etc. The annotation process is conducted by a team with a civil engineering background, consisting of more than 20 people. After consistency checks and expert reviews, the data enters the preprocessing stage, utilizing technologies such as format conversion, frame rate adjustment, and noise reduction. Data is stored in MP4 format organized in a tree structure to facilitate retrieval.This dataset is known for its high annotation accuracy and consistency, with annotation errors controlled within 5%. It innovatively employs a multimodal annotation method combining image and sound information for construction behavior recognition. This technology enhances the monitoring system's analytical precision, improving project progress prediction accuracy by over 20%. Compared to other similar datasets with poor generality, its data has higher coverage and detail richness, and good generality, suitable for various construction scenes. In terms of scale, quality, and technological innovation, this dataset significantly surpasses existing datasets on the market.