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Vision-Based Automated Recognition and 3D Localization Framework for Tower Cranes Using Far-Field Cameras.

Jiyao Wang¹, Qilin Zhang¹, Bin Yang¹

¹Department of Structural Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China.

Sensors (Basel, Switzerland)

|July 11, 2023

Summary

This study introduces a computer vision framework for real-time tower crane and hook localization using monocular cameras. The system enhances construction safety by accurately estimating crane orientation and hook position from arbitrary views.

Keywords:

computer vision far-field camera sensing system three-dimensional localization tower crane

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Area of Science:

Computer Vision
Robotics
Construction Engineering

Background:

Tower cranes pose significant safety risks on construction sites due to their large operational area and potential for collisions.
Accurate, real-time orientation and location data for tower cranes and hooks are crucial for mitigating these risks.
Existing computer vision-based (CVB) localization methods often struggle with ground-plane limitations or specific viewpoint dependencies.

Purpose of the Study:

To propose a novel framework for real-time recognition and 3D localization of tower cranes and hooks using monocular far-field cameras.
To enable accurate pose estimation of tower cranes from arbitrary viewpoints, addressing limitations of current methods.
To enhance construction safety management and productivity analysis through precise monitoring.

Main Methods:

A four-step framework involving far-field camera autocalibration (feature matching, horizon-line detection).
Deep learning-based segmentation for tower crane identification and geometric feature reconstruction.
3D localization estimation and pose estimation using monocular far-field cameras with arbitrary views.

Main Results:

The proposed framework achieves high precision in estimating both crane jib orientation and hook position.
Experimental validation on diverse construction site scenarios demonstrates the system's robustness and accuracy.
Performance was compared against ground-truth data obtained from sensors, confirming reliability.

Conclusions:

The developed framework effectively addresses the challenge of real-time tower crane and hook localization using monocular far-field cameras.
Accurate pose estimation from arbitrary views is a key contribution, advancing non-invasive safety monitoring.
The system holds significant potential for improving safety management and productivity analysis in construction environments.