# Ensemble of Deep Convolutional Neural Networks for Automatic Pavement Crack Detection and Measurement

arXiv: https://arxiv.org/abs/2002.03241 · HTML: https://mlsanity.com/paper/2002.03241/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2020.

## Built on by (most cited first, 8 of 8)

- [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)
- [Concrete Surface Crack Detection with Convolutional-based Deep Learning Models](https://mlsanity.com/paper/2401.07124/) (2024)
- [RHA-Net: An Encoder-Decoder Network with Residual Blocks and Hybrid Attention Mechanisms for Pavement Crack Segmentation](https://mlsanity.com/paper/2207.14166/) (2022)
- [CrackCLF: Automatic Pavement Crack Detection based on Closed-Loop Feedback](https://mlsanity.com/paper/2311.11815/) (2023)
- [Deep Learning for Pavement Condition Evaluation Using Satellite Imagery](https://mlsanity.com/paper/2508.01206/) (2025)
- [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)
- [Multi-Task Crack Foundation Model for Engineering-Reliable Crack Representation and Topology Preservation in Civil Infrastructure](https://mlsanity.com/paper/2606.05641/) (2026)

## Abstract (arXiv)

Automated pavement crack detection and measurement are important road issues. Agencies have to guarantee the improvement of road safety. Conventional crack detection and measurement algorithms can be extremely time-consuming and low efficiency. Therefore, recently, innovative algorithms have received increased attention from researchers. In this paper, we propose an ensemble of convolutional neural networks (without a pooling layer) based on probability fusion for automated pavement crack detection and measurement. Specifically, an ensemble of convolutional neural networks was employed to identify the structure of small cracks with raw images. Secondly, outputs of the individual convolutional neural network model for the ensemble were averaged to produce the final crack probability value of each pixel, which can obtain a predicted probability map. Finally, the predicted morphological features of the cracks were measured by using the skeleton extraction algorithm. To validate the proposed method, some experiments were performed on two public crack databases (CFD and AigleRN) and the results of the different state-of-the-art methods were compared. The experimental results show that the proposed method outperforms the other methods. For crack measurement, the crack length and width can be measure based on different crack types (complex, common, thin, and intersecting cracks.). The results show that the proposed algorithm can be effectively applied for crack measurement.