Semantic segmentation-based framework for concrete pouring progress monitoring by using multiple surveillance cameras

Abstract

Traditional construction progress monitoring methods face challenges in real-time monitoring of concrete pouring due to performance limitations and issues with registration and occlusion. This study proposes a framework based on multi-camera semantic fusion for monitoring the progress of concrete pouring. The construction site images are first segmented into semantic probabilities by a deep neural network. Dempster-Shafer evidence theory is then applied for semantic fusion, providing a comprehensive depiction of the progress. To reduce registration errors with the Building Information Model (BIM), a real-time perspective transformation algorithm is proposed to compensate for slight camera movements. Finally, a semantic inference method based on fully-connected Conditional Random Fields (CRFs) is employed to address occlusion by leveraging semantic context and the BIM floor plan. Comparative analysis confirmed these modules’ performance, with a remarkable reduction in relative error from 9.60% to 0.26%, enabling great potential for the continuous real-time monitoring of concrete pouring progress.