# SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D data

arXiv: https://arxiv.org/abs/2205.13326 · HTML: https://mlsanity.com/paper/2205.13326/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 53 references that are in the map.
- First posted on arXiv 26 May 2022.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [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)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision](https://mlsanity.com/paper/2106.01226/) (2021)
- [fastai: A Layered API for Deep Learning](https://mlsanity.com/paper/2002.04688/) (2020)
- [A Novel Focal Tversky loss function with improved Attention U-Net for lesion segmentation](https://mlsanity.com/paper/1810.07842/) (2018)
- [Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection](https://mlsanity.com/paper/1901.06340/) (2019)
- [Macro F1 and Macro F1](https://mlsanity.com/paper/1911.03347/) (2019)
- [Pothole Detection Based on Disparity Transformation and Road Surface Modeling](https://mlsanity.com/paper/1908.00894/) (2019)
- [Road surface 3d reconstruction based on dense subpixel disparity map estimation](https://mlsanity.com/paper/1807.01874/) (2018)
- [Road Damage Detection Based on Unsupervised Disparity Map Segmentation](https://mlsanity.com/paper/1910.04988/) (2019)
- [Layer-Specific Adaptive Learning Rates for Deep Networks](https://mlsanity.com/paper/1510.04609/) (2015)
- [We Learn Better Road Pothole Detection: from Attention Aggregation to Adversarial Domain Adaptation](https://mlsanity.com/paper/2008.06840/) (2020)
- [n-CPS: Generalising Cross Pseudo Supervision to n Networks for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2112.07528/) (2021)
- [High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks](https://mlsanity.com/paper/1604.04339/) (2016)
- [CNN-based Semantic Segmentation using Level Set Loss](https://mlsanity.com/paper/1910.00950/) (2019)
- [Learning Euler's Elastica Model for Medical Image Segmentation](https://mlsanity.com/paper/2011.00526/) (2020)

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

- [Road Surface Defect Detection -- From Image-based to Non-image-based: A Survey](https://mlsanity.com/paper/2402.04297/) (2024)
- [Flexible visual prompts for in-context learning in computer vision](https://mlsanity.com/paper/2312.06592/) (2023)
- [What Demands Attention in Urban Street Scenes? From Scene Understanding towards Road Safety: A Survey of Vision-driven Datasets and Studies](https://mlsanity.com/paper/2507.06513/) (2025)
- [CARD: A Multi-Modal Automotive Dataset for Dense 3D Reconstruction in Challenging Road Topography](https://mlsanity.com/paper/2605.05014/) (2026)
- [RoadFusion: Latent Diffusion Model for Pavement Defect Detection](https://mlsanity.com/paper/2507.15346/) (2025)
- [Automated Monitoring of Cultural Heritage Artifacts Using Semantic Segmentation](https://mlsanity.com/paper/2511.20541/) (2025)

## Abstract (arXiv)

This paper describes the methods submitted for evaluation to the SHREC 2022 track on pothole and crack detection in the road pavement. A total of 7 different runs for the semantic segmentation of the road surface are compared, 6 from the participants plus a baseline method. All methods exploit Deep Learning techniques and their performance is tested using the same environment (i.e.: a single Jupyter notebook). A training set, composed of 3836 semantic segmentation image/mask pairs and 797 RGB-D video clips collected with the latest depth cameras was made available to the participants. The methods are then evaluated on the 496 image/mask pairs in the validation set, on the 504 pairs in the test set and finally on 8 video clips. The analysis of the results is based on quantitative metrics for image segmentation and qualitative analysis of the video clips. The participation and the results show that the scenario is of great interest and that the use of RGB-D data is still challenging in this context.