# End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales

arXiv: https://arxiv.org/abs/2011.03098 · HTML: https://mlsanity.com/paper/2011.03098/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [Engineering deep learning methods on automatic detection of damage in infrastructure due to extreme events](https://mlsanity.com/paper/2205.02125/) (2022)
- [Automatic Displacement and Vibration Measurement in Laboratory Experiments with A Deep Learning Method](https://mlsanity.com/paper/2109.04960/) (2021)
- [3D vision-based structural masonry damage detection](https://mlsanity.com/paper/2308.16380/) (2023)
- [Deep Learning in Earthquake Engineering: A Comprehensive Review](https://mlsanity.com/paper/2405.09021/) (2024)
- [Deep learning-based automated damage detection in concrete structures using images from earthquake events](https://mlsanity.com/paper/2510.21063/) (2025)
- [Survey on Disaster Management Datasets for Remote Sensing Based Emergency Applications](https://mlsanity.com/paper/2605.08196/) (2026)

## Abstract (arXiv)

Robust Mask R-CNN (Mask Regional Convolu-tional Neural Network) methods are proposed and tested for automatic detection of cracks on structures or their components that may be damaged during extreme events, such as earth-quakes. We curated a new dataset with 2,021 labeled images for training and validation and aimed to find end-to-end deep neural networks for crack detection in the field. With data augmentation and parameters fine-tuning, Path Aggregation Network (PANet) with spatial attention mechanisms and High-resolution Network (HRNet) are introduced into Mask R-CNNs. The tests on three public datasets with low- or high-resolution images demonstrate that the proposed methods can achieve a big improvement over alternative networks, so the proposed method may be sufficient for crack detection for a variety of scales in real applications.