# Crack Detection Using Enhanced Hierarchical Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1912.12139 · HTML: https://mlsanity.com/paper/1912.12139/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2019.

## Built on by (most cited first, 3 of 3)

- [Hierarchical Convolutional Neural Network with Feature Preservation and Autotuned Thresholding for Crack Detection](https://mlsanity.com/paper/2104.10511/) (2021)
- [PointCrack3D: Crack Detection in Unstructured Environments using a 3D-Point-Cloud-Based Deep Neural Network](https://mlsanity.com/paper/2111.11615/) (2021)
- [SCNet: A Generalized Attention-based Model for Crack Fault Segmentation](https://mlsanity.com/paper/2112.01426/) (2021)

## Abstract (arXiv)

Unmanned aerial vehicles (UAV) are expected to replace human in hazardous tasks of surface inspection due to their flexibility in operating space and capability of collecting high quality visual data. In this study, we propose enhanced hierarchical convolutional neural networks (HCNN) to detect cracks from image data collected by UAVs. Unlike traditional HCNN, here a set of branch networks is utilised to reduce the obscuration in the down-sampling process. Moreover, the feature preserving blocks combine the current and previous terms from the convolutional blocks to provide input to the loss functions. As a result, the weights of resized images can be reduced to minimise the information loss. Experiments on images of different crack datasets have been carried out to demonstrate the effectiveness of proposed HCNN.