Reconstructing as-built beam bridge geometry from construction drawings using deep learning-based symbol pose estimation
Benedikt Faltin, Phillip Schönfelder, Damaris Gann, Markus König
Abstract
Efficient maintenance planning and streamlined inspection for bridges are essential to prevent catastrophic structural failures. Digital Bridge Management Systems (BMS) have the potential to streamline these tasks. However, their effectiveness relies heavily on the availability of accurate digital bridge models, which are currently challenging and costly to create, limiting the widespread adoption of BMS. This study addresses this issue by proposing a computer vision-based process for generating bridge superstructure models from pixel-based construction drawings. We introduce an automatic pipeline that utilizes a deep learning-based symbol pose estimation approach based on Keypoint R-CNN to organize drawing views spatially, implementing parts of the proposed process. By extending the keypoint-based detection approach to simultaneously process multiple object classes with a variable number of keypoints, a single instance of Keypoint R-CNN can be trained for all identified symbols. We conducted an empirical analysis to determine evaluation parameters for the symbol pose estimation approach to evaluate the method’s performance and improve the trained model’s comparability . Our findings demonstrate promising steps towards efficient bridge modeling, ultimately facilitating maintenance planning and management.