# Optimized Deep Encoder-Decoder Methods for Crack Segmentation

arXiv: https://arxiv.org/abs/2008.06266 · HTML: https://mlsanity.com/paper/2008.06266/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2020.

## Built on by (most cited first, 10 of 10)

- [CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and Frameworks](https://mlsanity.com/paper/2208.13054/) (2022)
- [CrackNex: a Few-shot Low-light Crack Segmentation Model Based on Retinex Theory for UAV Inspections](https://mlsanity.com/paper/2403.03063/) (2024)
- [Hybrid-Segmentor: A Hybrid Approach to Automated Fine-Grained Crack Segmentation in Civil Infrastructure](https://mlsanity.com/paper/2409.02866/) (2024)
- [Weakly-Supervised Surface Crack Segmentation by Generating Pseudo-Labels using Localization with a Classifier and Thresholding](https://mlsanity.com/paper/2109.00456/) (2021)
- [What's Cracking? A Review and Analysis of Deep Learning Methods for Structural Crack Segmentation, Detection and Quantification](https://mlsanity.com/paper/2202.03714/) (2022)
- [Road Surface Defect Detection -- From Image-based to Non-image-based: A Survey](https://mlsanity.com/paper/2402.04297/) (2024)
- [SCNet: A Generalized Attention-based Model for Crack Fault Segmentation](https://mlsanity.com/paper/2112.01426/) (2021)
- [Optimized Hybrid Focal Margin Loss for Crack Segmentation](https://mlsanity.com/paper/2302.04395/) (2023)
- [Detection of Pavement Cracks by Deep Learning Models of Transformer and UNet](https://mlsanity.com/paper/2304.12596/) (2023)
- [Deep Learning for Segmentation of Cracks in High-Resolution Images of Steel Bridges](https://mlsanity.com/paper/2403.17725/) (2024)

## Abstract (arXiv)

Surface crack segmentation poses a challenging computer vision task as background, shape, colour and size of cracks vary. In this work we propose optimized deep encoder-decoder methods consisting of a combination of techniques which yield an increase in crack segmentation performance. Specifically we propose a decoder-part for an encoder-decoder based deep learning architecture for semantic segmentation and study its components to achieve increased performance. We also examine the use of different encoder strategies and introduce a data augmentation policy to increase the amount of available training data. The performance evaluation of our method is carried out on four publicly available crack segmentation datasets. Additionally, we introduce two techniques into the field of surface crack segmentation, previously not used there: Generating results using test-time-augmentation and performing a statistical result analysis over multiple training runs. The former approach generally yields increased performance results, whereas the latter allows for more reproducible and better representability of a methods results. Using those aforementioned strategies with our proposed encoder-decoder architecture we are able to achieve new state of the art results in all datasets.