Automatic detection of pavement cracks is an important task in transportation maintenance for driving safety assurance. 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 pavement and possible shadows with similar intensity. Inspired by recent success on applying deep learning to computer vision and medical problems, a deep-learning based method for crack detection is proposed in this paper. A supervised deep convolutional neural network is trained to classify each image patch in the collected images. Quantitative evaluation conducted on a data set of 500 images of size 3264 χ 2448, collected by a low-cost smart phone, demonstrates that the learned deep features with the proposed deep learning framework provide superior crack detection performance when compared with features extracted with existing hand-craft methods.

Road crack detection using deep convolutional neural network

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

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

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.

Road condition acquisition and assessment are the key to guarantee their permanent availability. In order to maintain a country's whole road network, millions of high-resolution images have to be analyzed annually. Currently, this requires cost and time excessive manual labor. We aim to automate this process to a high degree by applying deep neural networks. Such networks need a lot of data to be trained successfully, which are not publicly available at the moment. In this paper, we present the GAPs dataset, which is the first freely available pavement distress dataset of a size, large enough to train high-performing deep neural networks. It provides high quality images, recorded by a standardized process fulfilling German federal regulations, and detailed distress annotations. For the first time, this enables a fair comparison of research in this field. Furthermore, we present a first evaluation of the state of the art in pavement distress detection and an analysis of the effectiveness of state of the art regularization techniques on this dataset.

How to get pavement distress detection ready for deep learning? A systematic approach

Pavement cracks involve important information to measure road quality. Crack classification is a challenging problem given the diversity of possible cracks, therefore, it is needed to retrieve good features in order to facilitate the learning of predictive models with as few samples as possible. In this paper, we propose a graph-based set of features to efficiently describe cracks. These features proved to have high degree of expressiveness and robustness when used for crack classification. We show that the proposed features succeed in the assessment of 525 images with different kinds of cracks. We proved the robustness of the approach applying different levels of noise to the images and evaluating the classification accuracy.

Pavement pathologies classification using graph-based features

A key issue in video object tracking is the representation of the objects and how effectively it discriminates between different objects. Several techniques have been proposed, but without a generally accepted method. While analysis and comparisons of these individual methods have been presented in the literature, their evaluation as part of a global solution has been overlooked. The appearance model for the objects is a component of a video object tracking framework, depending on previous processing stages and affecting those that succeed it. As a result, these interdependencies should be taken into account when analysing the performance of the object description techniques. We propose an integrated analysis of object descriptors and appearance models through their comparison in a common object tracking solution. The goal is to contribute to a better understanding of object description methods and their impact on the tracking process. Our contributions are threefold: propose a novel descriptor evaluation and characterisation paradigm; perform the first integrated analysis of state-of-the-art description methods in a scenario of people tracking; put forward some ideas for appearance models to use in this context. This work provides foundations for future tests and the proposed assessment approach contributes to the informed selection of techniques more adequately for a given tracking application context.

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Analysis of object description methods in a video object tracking environment

The increasing demand for human activity analysis in surveillance scenarios has been triggered by the emergence of new features and concepts to help in identifying activities of interest. However, the characterisation of individual and group behaviours is a topic not so well studied in the video surveillance community due to not only its intrinsic difficulty and large variety of topics involved, but also because of the lack of valid semantic concepts that relate human activity to social context. In this paper, we address the topic of social semantic meaning in a well-defined surveillance scenario, namely shopping mall, and propose new definitions of individual and group behaviour that consider environment context, a relational descriptor that emphasises position and attention-based characteristics, and a new classification approach based on mini-batches. We also present a wide evaluation process that analyses the sociological meaning of the individual features and outlines the performance impact of automatic features extraction processes into our classification framework. We verify the discriminative value of the selected features, state the descriptor performance and robustness over different stress conditions, confirm the advantage of the proposed mini-batch classification approach which obtains promising results, and outline future research lines to improve our novel social behavioural analysis framework.

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Cross-layer classification framework for automatic social behavioural analysis in surveillance scenario

Group behaviour characterisation is a topic not so well studied in the video surveillance community due to its difficulty and large variety of topics involved, but mainly because the lack of valid semantic concepts that relate collective activity to social context. In this work, our proposal is three-fold: a new definition of semantic concepts for social group analysis considering environment context, a novel video surveillance dataset that conveys a sociological perspective, and a descriptor that emphasises social interactions cues within a group. Promising results were revealed in order to deal with such complex problem.

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Social Signaling Descriptor for Group Behaviour Analysis

The increasing demand for human activity analysis on surveillance scenarios has been provoking the emerging of new features and concepts that could help to identify the activities of interest. In this paper, we present a context-based descriptor to identify individual profiles. It accounts with a multi-scale histogram representation of position-based and attention-based features that follow a key-point trajectory sampling. The notion of profile is expressed by a new semantic concept introduced as an adjective for action recognition. We also identify a very rich dataset, in terms of intensity and variability of human activity, and extended it by manual annotation to validate the introduced concept of profile and test the descriptor’s discriminative power. High rates of recognition were achieved.

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Lucian Ciobanu

Papers

Pavement pathologies classification using graph-based features

Analysis of object description methods in a video object tracking environment

Cross-layer classification framework for automatic social behavioural analysis in surveillance scenario

Social Signaling Descriptor for Group Behaviour Analysis

Context-based Trajectory Descriptor for Human Activity Profiling