# Automated Pavement Crack Segmentation Using U-Net-based Convolutional Neural Network

arXiv: https://arxiv.org/abs/2001.01912 · HTML: https://mlsanity.com/paper/2001.01912/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2020.

## Built on by (most cited first, 2 of 2)

- [Damage detection using in-domain and cross-domain transfer learning](https://mlsanity.com/paper/2102.03858/) (2021)
- [Pavement Distress Detection and Segmentation using YOLOv4 and DeepLabv3 on Pavements in the Philippines](https://mlsanity.com/paper/2103.06467/) (2021)

## Abstract (arXiv)

Automated pavement crack image segmentation is challenging because of inherent irregular patterns, lighting conditions, and noise in images. Conventional approaches require a substantial amount of feature engineering to differentiate crack regions from non-affected regions. In this paper, we propose a deep learning technique based on a convolutional neural network to perform segmentation tasks on pavement crack images. Our approach requires minimal feature engineering compared to other machine learning techniques. We propose a U-Net-based network architecture in which we replace the encoder with a pretrained ResNet-34 neural network. We use a "one-cycle" training schedule based on cyclical learning rates to speed up the convergence. Our method achieves an F1 score of 96% on the CFD dataset and 73% on the Crack500 dataset, outperforming other algorithms tested on these datasets. We perform ablation studies on various techniques that helped us get marginal performance boosts, i.e., the addition of spatial and channel squeeze and excitation (SCSE) modules, training with gradually increasing image sizes, and training various neural network layers with different learning rates.