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  4. Vol 9, Issue 1, 2025

Research on Real-Time Collision Detection System for Data-Driven Tower Crane Simulation Monitoring and Visual Optimization

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DOI: 10.23977/cpcs.2025.090101 | Downloads: 16 | Views: 507

Author(s)

Jinshuo Zhang 1

Affiliation(s)

1 Washington University of St.Louis, Saint Louis, 63105, Missouri, USA

Corresponding Author

Jinshuo Zhang

ABSTRACT

In order to improve the safety and efficiency of tower crane operation, a data-driven real-time collision detection system is proposed in this paper, which combines simulation monitoring and vision optimization technology. The research team collected operational data in real time through sensors and cameras, and used convolutional neural networks (CNN) to extract and classify image features to quickly identify potential collision risks. The experimental results show that the maximum detection accuracy of the system can reach 96.87% under various working conditions, but the missed detection rate and false alarm rate are reduced by 21.43% and 87.65% respectively compared with the traditional method. Therefore, this system provides an innovative solution for the safety management of tower cranes and promotes further development in the field of intelligent buildings. 

KEYWORDS

Data-driven, Real-time collision detection, Convolutional neural network (CNN), Vision optimization, Simulation monitoring

CITE THIS PAPER

Jinshuo Zhang, Research on Real-Time Collision Detection System for Data-Driven Tower Crane Simulation Monitoring and Visual Optimization. Computing, Performance and Communication Systems (2025) Vol. 9: 1-7. DOI: http://dx.doi.org/10.23977/cpcs.2025.090101.

REFERENCES

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