Cracks are a growing threat to road conditions and have drawn much attention to the construction of intelligent transportation systems. However, as the key part of an intelligent transportation system, automatic road crack detection has been challenged because of the intense inhomogeneity along the cracks, the topology complexity of cracks, the inference of noises with similar texture to the cracks, and so on. In this paper, we propose CrackForest, a novel road crack detection framework based on random structured forests, to address these issues. Our contributions are shown as follows: 1) apply the integral channel features to redefine the tokens that constitute a crack and get better representation of the cracks with intensity inhomogeneity; 2) introduce random structured forests to generate a high-performance crack detector, which can identify arbitrarily complex cracks; and 3) propose a new crack descriptor to characterize cracks and discern them from noises effectively. In addition, our method is faster and easier to parallel. Experimental results prove the state-of-the-art detection precision of CrackForest compared with competing methods.

Automatic Road Crack Detection Using Random Structured Forests

Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection

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. The first task addressed, i.e., crack detection, is based on a learning from samples paradigm, where a subset of the available image database is automatically selected and used for unsupervised training of the system. The system classifies nonoverlapping image blocks as either containing crack pixels or not. The second task deals with crack type characterization, for which another classification system is constructed, to characterize the detected cracks' connect components. Cracks are labeled according to the types defined in the Portuguese Distress Catalog, with each different crack present in a given image receiving the appropriate label. Moreover, a novel methodology for the assignment of crack severity levels is introduced, computing an estimate for the width of each detected crack. Experimental crack detection and characterization results are presented based on images captured during a visual road pavement surface survey over Portuguese roads, with promising results. This is shown by the quantitative evaluation methodology introduced for the evaluation of this type of system, including a comparison with human experts' manual labeling results.

Automatic Road Crack Detection and Characterization

DeepCrack: A deep hierarchical feature learning architecture for crack segmentation

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 a 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 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. In addition, we propose a novel measurement for crack detection named average intersection over union (AIU). To demonstrate the superiority and generalizability of the proposed method, we evaluate it on five crack datasets and compare it with the state-of-the-art crack detection, edge detection, and semantic segmentation methods. The extensive experiments show that the proposed method outperforms these methods in terms of accuracy and generalizability. Code and data can be found in https://github.com/fyangneil/pavement-crack-detection .

Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection

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.

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Automatic Road Pavement Assessment with Image Processing: Review and Comparison

La detection automatique de fissures de chaussees est un tâche cruciale dans de nombreux pays. En effet, elle permet la qualite de la surface des chaussees dans le but de determiner les ameliorations necessaires. De nombreuses methodes ont ete introduire pour realiser la detection de defauts de surfaces et, en particulier, les fissures : en utilisant des outils de morphologie mathematiques, des reseaux de neurones ou un filtrage multi-echelle. Le filtrage multi-echelle semble le plus approprie et c'est la raison pour laquelle notre travail porte sur la validation d'une methode a base de decomposition en ondelettes utilisee comme initialisation a une segmentation par modelisation markovienne. De nos jours, il n'existe pas d'outils permettant de comparer et d'evaluer precisement les performances et avantages des methodes, d'une part, et de quantifier l'efficacite d'une methode comparee a l'etat de l'art. En consequence, le deuxieme objectif de ce travail est d'introduire un protocole d'evaluation afin de valider la methode proposee et de decrire comment fixer les parametres.

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Jean Marc Moliard

Papers

Automatic Road Pavement Assessment with Image Processing: Review and Comparison

ROAD CRACK EXTRACTION WITH ADAPTED FILTERING AND MARKOV MODEL-BASED SEGMENTATION - Introduction and Validation