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Related Experiment Video

Updated: Jun 29, 2026

Operation of the Collaborative Composite Manufacturing CCM System

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

Two-Stage Marker Detection-Localization Network for Bridge-Erecting Machine Hoisting Alignment.

Lei Li¹, Zelong Xiao¹, Taiyang Hu¹

¹School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.

Sensors (Basel, Switzerland)

|September 13, 2025

Summary

This study introduces a novel two-stage network for precise marker detection and localization in complex construction environments, improving automated hoisting alignment for bridge-erecting machines.

Keywords:

Transformer homography estimation bridge-erecting machine alignment two-stage detection–localization

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Published on: October 27, 2023

Area of Science:

Computer Vision
Robotics
Construction Engineering

Background:

Construction environments present significant challenges for automated systems, including lighting variations, occlusions, and marker contamination.
High-precision alignment is crucial for the safe and efficient hoisting operations of bridge-erecting machines.

Purpose of the Study:

To develop a robust and accurate marker detection and localization network for hoisting alignment in complex construction settings.
To enhance the precision and reliability of automated hoisting control in bridge-erecting machines.

Main Methods:

A two-stage "coarse detection-fine estimation" network featuring a dynamic hybrid backbone (DHB) for efficient marker region localization.
A Transformer-based homography estimation module utilizing multi-head self-attention for precise alignment.
A multi-dimensional data augmentation strategy to address data scarcity in construction scenes.

Main Results:

Achieved 97.8% detection accuracy (mAP) and a homography estimation reprojection error under 1.2 mm.
Demonstrated a processing frame rate of 32 FPS, suitable for real-time applications.
Significantly improved alignment precision and robustness compared to traditional methods in complex environments.

Conclusions:

The proposed network effectively overcomes challenges in complex construction environments for marker detection and localization.
It provides reliable technical support for precise control in automated hoisting operations of bridge-erecting machines.
The method offers enhanced robustness and precision, paving the way for more advanced automated construction.