# Dual flow fusion model for concrete surface crack segmentation

arXiv: https://arxiv.org/abs/2305.05132 · HTML: https://mlsanity.com/paper/2305.05132/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 27 references that are in the map.
- First posted on arXiv 9 May 2023.

## Builds on (most cited first, 18 of 18)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [CvT: Introducing Convolutions to Vision Transformers](https://mlsanity.com/paper/2103.15808/) (2021)
- [Segmenter: Transformer for Semantic Segmentation](https://mlsanity.com/paper/2105.05633/) (2021)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs](https://mlsanity.com/paper/1412.7062/) (2014)
- [CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows](https://mlsanity.com/paper/2107.00652/) (2021)
- [Conformer: Local Features Coupling Global Representations for Visual Recognition](https://mlsanity.com/paper/2105.03889/) (2021)
- [Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding](https://mlsanity.com/paper/1904.08582/) (2019)
- [Dual-stream Network for Visual Recognition](https://mlsanity.com/paper/2105.14734/) (2021)
- [SPP-Net: Deep Absolute Pose Regression with Synthetic Views](https://mlsanity.com/paper/1712.03452/) (2017)

## Abstract (arXiv)

The existence of cracks and other damages pose a significant threat to the safe operation of transportation infrastructure. Traditional manual detection and ultrasound equipment testing consume a lot of time and resources. With the development of deep learning technology, many deep learning models have been widely applied to practical visual segmentation tasks. The detection method based on deep learning models has the advantages of high detection accuracy, fast detection speed, and simple operation. However, deep learning-based crack segmentation models are sensitive to background noise, have rough edges, and lack robustness. Therefore, this paper proposes a crack segmentation model based on the fusion of dual streams. The image is inputted simultaneously into two designed processing streams to independently extract long-distance dependence and local detail features. The adaptive prediction is achieved through the dual-headed mechanism. Meanwhile, a novel interaction fusion mechanism is proposed to guide the complementary of different feature layers to achieve crack location and recognition in complex backgrounds. Finally, an edge optimization method is proposed to improve the accuracy of segmentation. Experiments show that the F1 value of segmentation results on the DeepCrack[1] public dataset is 93.7% and the IOU value is 86.6%. The F1 value of segmentation results on the CRACK500[2] dataset is 78.1%, and the IOU value is 66.0%.