Post-disaster building indoor damage and survivor detection using autonomous path planning and deep learning with unmanned aerial vehicles
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866929757545824256 |
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| author | Pan, Xiao Tavasoli, Sina Yang, T. Y. Poorghasem, Sina |
| author_facet | Pan, Xiao Tavasoli, Sina Yang, T. Y. Poorghasem, Sina |
| contents | Rapid response to natural disasters such as earthquakes is a crucial element in ensuring the safety of civil infrastructures and minimizing casualties. Traditional manual inspection is labour-intensive, time-consuming, and can be dangerous for inspectors and rescue workers. This paper proposed an autonomous inspection approach for structural damage inspection and survivor detection in the post-disaster building indoor scenario, which incorporates an autonomous navigation method, deep learning-based damage and survivor detection method, and a customized low-cost micro aerial vehicle (MAV) with onboard sensors. Experimental studies in a pseudo-post-disaster office building have shown the proposed methodology can achieve high accuracy in structural damage inspection and survivor detection. Overall, the proposed inspection approach shows great potential to improve the efficiency of existing manual post-disaster building inspection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_10027 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Post-disaster building indoor damage and survivor detection using autonomous path planning and deep learning with unmanned aerial vehicles Pan, Xiao Tavasoli, Sina Yang, T. Y. Poorghasem, Sina Computer Vision and Pattern Recognition Robotics Rapid response to natural disasters such as earthquakes is a crucial element in ensuring the safety of civil infrastructures and minimizing casualties. Traditional manual inspection is labour-intensive, time-consuming, and can be dangerous for inspectors and rescue workers. This paper proposed an autonomous inspection approach for structural damage inspection and survivor detection in the post-disaster building indoor scenario, which incorporates an autonomous navigation method, deep learning-based damage and survivor detection method, and a customized low-cost micro aerial vehicle (MAV) with onboard sensors. Experimental studies in a pseudo-post-disaster office building have shown the proposed methodology can achieve high accuracy in structural damage inspection and survivor detection. Overall, the proposed inspection approach shows great potential to improve the efficiency of existing manual post-disaster building inspection. |
| title | Post-disaster building indoor damage and survivor detection using autonomous path planning and deep learning with unmanned aerial vehicles |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2503.10027 |