# Post-disaster building indoor damage and survivor detection using autonomous path planning and deep learning with unmanned aerial vehicles

arXiv: https://arxiv.org/abs/2503.10027 · HTML: https://mlsanity.com/paper/2503.10027/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 13 Mar 2025.

## Builds on (most cited first, 6 of 6)

- [Image-based monitoring of bolt loosening through deep-learning-based integrated detection and tracking](https://mlsanity.com/paper/2111.09117/) (2021)
- [Postdisaster image-based damage detection and repair cost estimation of reinforced concrete buildings using dual convolutional neural networks](https://mlsanity.com/paper/2111.09862/) (2021)
- [A Comparison of Embedded Deep Learning Methods for Person Detection](https://mlsanity.com/paper/1812.03451/) (2018)
- [Accurate indoor mapping using an autonomous unmanned aerial vehicle (UAV)](https://mlsanity.com/paper/1808.01940/) (2018)
- [Implementation of Survivor Detection Strategies Using Drones](https://mlsanity.com/paper/2003.12559/) (2020)
- [Autonomous damage assessment of structural columns using low-cost micro aerial vehicles and multi-view computer vision](https://mlsanity.com/paper/2308.16278/) (2023)

## Abstract (arXiv)

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.