Automatic Road Pavement Assessment with Image Processing: Review and Comparison
Sylvie Chambon,Jean Marc Moliard +1 more
Reads0
Chats0
TLDR
An evaluation and comparison protocol which has been designed for evaluating this difficult task—the road pavement crack detection—is introduced and the proposed method is validated, analysed, and compared to a detection approach based on morphological tools.Abstract:
In the field of noninvasive sensing techniques for civil infrastructures monitoring, this paper addresses the problem of crack detection, in the surface of the French national roads, by automatic analysis of optical images. The first contribution is a state of the art of the image-processing tools applied to civil engineering. The second contribution is about fine-defect detection in pavement surface. The approach is based on a multi-scale extraction and a Markovian segmentation. Third, an evaluation and comparison protocol which has been designed for evaluating this difficult task—the road pavement crack detection—is introduced. Finally, the proposed method is validated, analysed, and compared to a detection approach based on morphological tools.read more
Citations
More filters
Journal ArticleDOI
Automatic Road Crack Detection Using Random Structured Forests
TL;DR: Experimental results prove the state-of-the-art detection precision of CrackForest compared with competing methods.
Journal ArticleDOI
Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection
TL;DR: A Deep Convolutional Neural Network trained on the ‘big data’ ImageNet database is employed to automatically detect cracks in Hot-Mix Asphalt and Portland Cement Concrete surfaced pavement images that also include a variety of non-crack anomalies and defects.
Journal ArticleDOI
Automatic Road Crack Detection and Characterization
TL;DR: A fully integrated system for the automatic detection and characterization of cracks in road flexible pavement surfaces, which does not require manually labeled samples, is proposed to minimize the human subjectivity resulting from traditional visual surveys.
Journal ArticleDOI
DeepCrack: A deep hierarchical feature learning architecture for crack segmentation
TL;DR: A deep hierarchical convolutional neural network (CNN) is proposed, called as DeepCrack, to predict pixel-wise crack segmentation in an end-to-end method using both guided filtering and Conditional Random Fields methods to refine the final prediction results.
Journal ArticleDOI
Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection
TL;DR: Yan et al. as mentioned in this paper proposed a novel network architecture, named feature pyramid and hierarchical boosting network (FPHBN), which integrates context information to low-level features for crack detection in a feature pyramid way, and it balances the contributions of both easy and hard samples to loss by nested sample reweighting in a hierarchical way during training.
References
More filters
Journal ArticleDOI
Changes in portlandite morphology with solvent composition: Atomistic simulations and experiment
TL;DR: In this article, a new analysis tool was developed to quantify the experimentally observed changes in morphology of portlandite, allowing the calculation of the relative surface energies of the crystal facets.
Journal ArticleDOI
Detection of blood vessels in retinal images using two-dimensional matched filters
TL;DR: The concept of matched filter detection of signals is used to detect piecewise linear segments of blood vessels in these images and the results are compared to those obtained with other methods.
Journal ArticleDOI
Analysis of edge-detection techniques for crack identification in bridges
TL;DR: This paper provides a comparison of the effectiveness of four crack-detection techniques: fast Haar transform (FHT), fast Fourier transform, Sobel, and Canny and shows that the FHT was significantly more reliable than the other three edge-detector techniques in identifying cracks.
Journal ArticleDOI
An active testing model for tracking roads in satellite images
Donald Geman,Bruno Jedynak +1 more
TL;DR: A new approach for tracking roads from satellite images, and thereby illustrate a general computational strategy for tracking 1D structures and other recognition tasks in computer vision, related to recent work in active vision and motivated by the "divide-and-conquer" strategy of parlour games.
Proceedings Article
Automatic road crack segmentation using entropy and image dynamic thresholding
TL;DR: This paper presents a novel framework for automatic crack detection and classification using survey images acquired at high driving speeds, using two image databases acquired using professional high speed equipment.