# Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection

arXiv: https://arxiv.org/abs/1901.06340 · HTML: https://mlsanity.com/paper/1901.06340/

## Key facts

- Cited by 80 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2019.

## Built on by (most cited first, 30 of 80)

- [RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth](https://mlsanity.com/paper/2108.00616/) (2021)
- [CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and Frameworks](https://mlsanity.com/paper/2208.13054/) (2022)
- [SCSegamba: Lightweight Structure-Aware Vision Mamba for Crack Segmentation in Structures](https://mlsanity.com/paper/2503.01113/) (2025)
- [Fine-tuning vision foundation model for crack segmentation in civil infrastructures](https://mlsanity.com/paper/2312.04233/) (2023)
- [A Convolutional-Transformer Network for Crack Segmentation with Boundary Awareness](https://mlsanity.com/paper/2302.11728/) (2023)
- [Real-time High-Resolution Neural Network with Semantic Guidance for Crack Segmentation](https://mlsanity.com/paper/2307.00270/) (2023)
- [SAM-based instance segmentation models for the automation of structural damage detection](https://mlsanity.com/paper/2401.15266/) (2024)
- [NHA12D: A New Pavement Crack Dataset and a Comparison Study Of Crack Detection Algorithms](https://mlsanity.com/paper/2205.01198/) (2022)
- [Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded Images](https://mlsanity.com/paper/2302.12491/) (2023)
- [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)
- [SemiCurv: Semi-Supervised Curvilinear Structure Segmentation](https://mlsanity.com/paper/2205.08706/) (2022)
- [SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D data](https://mlsanity.com/paper/2205.13326/) (2022)
- [UP-CrackNet: Unsupervised Pixel-Wise Road Crack Detection via Adversarial Image Restoration](https://mlsanity.com/paper/2401.15647/) (2024)
- [Staircase Cascaded Fusion of Lightweight Local Pattern Recognition and Long-Range Dependencies for Structural Crack Segmentation](https://mlsanity.com/paper/2408.12815/) (2024)
- [PaveSAM Segment Anything for Pavement Distress](https://mlsanity.com/paper/2409.07295/) (2024)
- [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)
- [Image2PCI -- A Multitask Learning Framework for Estimating Pavement Condition Indices Directly from Images](https://mlsanity.com/paper/2310.08538/) (2023)
- [Global-Regularized Neighborhood Regression for Efficient Zero-Shot Texture Anomaly Detection](https://mlsanity.com/paper/2406.07333/) (2024)
- [Enriching Information and Preserving Semantic Consistency in Expanding Curvilinear Object Segmentation Datasets](https://mlsanity.com/paper/2407.08209/) (2024)
- [Self-Supervised Multi-Scale Transformer with Attention-Guided Fusion for Efficient Crack Detection](https://mlsanity.com/paper/2510.10378/) (2025)
- [Rethinking Lightweight Convolutional Neural Networks for Efficient and High-quality Pavement Crack Detection](https://mlsanity.com/paper/2109.05707/) (2021)
- [B-BACN: Bayesian Boundary-Aware Convolutional Network for Crack Characterization](https://mlsanity.com/paper/2302.06827/) (2023)
- [Deep Learning Approaches in Pavement Distress Identification: A Review](https://mlsanity.com/paper/2308.00828/) (2023)
- [MixerCSeg: An Efficient Mixer Architecture for Crack Segmentation via Decoupled Mamba Attention](https://mlsanity.com/paper/2603.01361/) (2026)
- [Hierarchical Convolutional Neural Network with Feature Preservation and Autotuned Thresholding for Crack Detection](https://mlsanity.com/paper/2104.10511/) (2021)
- [Pavementscapes: a large-scale hierarchical image dataset for asphalt pavement damage segmentation](https://mlsanity.com/paper/2208.00775/) (2022)
- [Automated crack propagation measurement on asphalt concrete specimens using an optical flow-based deep neural network](https://mlsanity.com/paper/2303.05957/) (2023)
- [Automated Pavement Cracks Detection and Classification Using Deep Learning](https://mlsanity.com/paper/2406.07674/) (2024)
- [UCS: A Universal Model for Curvilinear Structure Segmentation](https://mlsanity.com/paper/2504.04034/) (2025)

## Abstract (arXiv)

Pavement crack detection is a critical task for insuring road safety. Manual crack detection is extremely time-consuming. Therefore, an automatic road crack detection method is required to boost this progress. However, it remains a challenging task due to the intensity inhomogeneity of cracks and complexity of the background, e.g., the low contrast with surrounding pavements and possible shadows with similar intensity. Inspired by recent advances of deep learning in computer vision, we propose a novel network architecture, named Feature Pyramid and Hierarchical Boosting Network (FPHBN), for pavement crack detection. The proposed network integrates semantic information to low-level features for crack detection in a feature pyramid way. And, it balances the contribution of both easy and hard samples to loss by nested sample reweighting in a hierarchical way. To demonstrate the superiority and generality of the proposed method, we evaluate the proposed method on five crack datasets and compare it with state-of-the-art crack detection, edge detection, semantic segmentation methods. Extensive experiments show that the proposed method outperforms these state-of-the-art methods in terms of accuracy and generality.