# Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding

arXiv: https://arxiv.org/abs/1904.08582 · HTML: https://mlsanity.com/paper/1904.08582/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2019.

## Built on by (most cited first, 30 of 33)

- [CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and Frameworks](https://mlsanity.com/paper/2208.13054/) (2022)
- [Computer Vision for Road Imaging and Pothole Detection: A State-of-the-Art Review of Systems and Algorithms](https://mlsanity.com/paper/2204.13590/) (2022)
- [Road Damage and Manhole Detection using Deep Learning for Smart Cities: A Polygonal Annotation Approach](https://mlsanity.com/paper/2510.03797/) (2025)
- [SAM-SP: Self-Prompting Makes SAM Great Again](https://mlsanity.com/paper/2408.12364/) (2024)
- [Weakly-Supervised Surface Crack Segmentation by Generating Pseudo-Labels using Localization with a Classifier and Thresholding](https://mlsanity.com/paper/2109.00456/) (2021)
- [UP-CrackNet: Unsupervised Pixel-Wise Road Crack Detection via Adversarial Image Restoration](https://mlsanity.com/paper/2401.15647/) (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)
- [From Classification to Segmentation with Explainable AI: A Study on Crack Detection and Growth Monitoring](https://mlsanity.com/paper/2309.11267/) (2023)
- [Evaluating Computer Vision Techniques for Urban Mobility on Large-Scale, Unconstrained Roads](https://mlsanity.com/paper/2109.05226/) (2021)
- [Multi-Scale Feature Fusion: Learning Better Semantic Segmentation for Road Pothole Detection](https://mlsanity.com/paper/2112.13082/) (2021)
- [Deep Learning Approaches in Pavement Distress Identification: A Review](https://mlsanity.com/paper/2308.00828/) (2023)
- [Pavement Distress Detection and Segmentation using YOLOv4 and DeepLabv3 on Pavements in the Philippines](https://mlsanity.com/paper/2103.06467/) (2021)
- [Hierarchical Convolutional Neural Network with Feature Preservation and Autotuned Thresholding for Crack Detection](https://mlsanity.com/paper/2104.10511/) (2021)
- [Computer-Aided Road Inspection: Systems and Algorithms](https://mlsanity.com/paper/2203.02355/) (2022)
- [A Multitask Deep Learning Model for Parsing Bridge Elements and Segmenting Defect in Bridge Inspection Images](https://mlsanity.com/paper/2209.02190/) (2022)
- [An Adaptive Structural Learning of Deep Belief Network for Image-based Crack Detection in Concrete Structures Using SDNET2018](https://mlsanity.com/paper/2110.12700/) (2021)
- [A Novel Approach for Deterioration and Damage Identification in Building Structures Based on Stockwell-Transform and Deep Convolutional Neural Network](https://mlsanity.com/paper/2111.06155/) (2021)
- [Characterization of Semantic Segmentation Models on Mobile Platforms for Self-Navigation in Disaster-Struck Zones](https://mlsanity.com/paper/2202.01421/) (2022)
- [Weakly-Supervised Crack Detection](https://mlsanity.com/paper/2206.06743/) (2022)
- [Kernel Inversed Pyramidal Resizing Network for Efficient Pavement Distress Recognition](https://mlsanity.com/paper/2212.01790/) (2022)
- [Multi-class Road Defect Detection and Segmentation using Spatial and Channel-wise Attention for Autonomous Road Repairing](https://mlsanity.com/paper/2402.04064/) (2024)
- [Vehicular Road Crack Detection with Deep Learning: A New Online Benchmark for Comprehensive Evaluation of Existing Algorithms](https://mlsanity.com/paper/2503.18082/) (2025)
- [Deep Transfer Learning for Identifications of Slope Surface Cracks](https://mlsanity.com/paper/2108.04235/) (2021)
- [LEA-Net: Layer-wise External Attention Network for Efficient Color Anomaly Detection](https://mlsanity.com/paper/2109.05493/) (2021)
- [A Computer Vision-assisted Approach to Automated Real-Time Road Infrastructure Management](https://mlsanity.com/paper/2202.13285/) (2022)
- [A Pre-study on Data Processing Pipelines for Roadside Object Detection Systems Towards Safer Road Infrastructure](https://mlsanity.com/paper/2205.01783/) (2022)
- [YOLOv5s-GTB: light-weighted and improved YOLOv5s for bridge crack detection](https://mlsanity.com/paper/2206.01498/) (2022)
- [Dual flow fusion model for concrete surface crack segmentation](https://mlsanity.com/paper/2305.05132/) (2023)
- [Edge Storage Management Recipe with Zero-Shot Data Compression for Road Anomaly Detection](https://mlsanity.com/paper/2307.04298/) (2023)
- [Continual-learning-based framework for structural damage recognition](https://mlsanity.com/paper/2408.15513/) (2024)

## Abstract (arXiv)

Crack is one of the most common road distresses which may pose road safety hazards. Generally, crack detection is performed by either certified inspectors or structural engineers. This task is, however, time-consuming, subjective and labor-intensive. In this paper, we propose a novel road crack detection algorithm based on deep learning and adaptive image segmentation. Firstly, a deep convolutional neural network is trained to determine whether an image contains cracks or not. The images containing cracks are then smoothed using bilateral filtering, which greatly minimizes the number of noisy pixels. Finally, we utilize an adaptive thresholding method to extract the cracks from road surface. The experimental results illustrate that our network can classify images with an accuracy of 99.92%, and the cracks can be successfully extracted from the images using our proposed thresholding algorithm.