Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection

Yang, Fan; Zhang, Lei; Yu, Sijia; Prokhorov, Danil; Mei, Xue; Ling, Haibin

doi:10.1109/tits.2019.2910595

Cited by 581 publications

(392 citation statements)

References 51 publications

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“…In Table 7, it is clear that proposed U-HDN achieves high performance compared with other algorithms in terms of ODS and OIS. Methods ODS OIS HED [74] 0.042 0.626 RCF [64] 0.462 0.607 FCN [82] 0.322 0.609 CrackForest [44] 0.231 0.104 FPHBN [78] 0.492 0.705 U-net [65] 0.752 0.897 U-HDN 0.783 0.928 U-HDN (only using AigleRN) 0.927 0.912…”

Section: Aiglern Dataset Generalizationmentioning

confidence: 99%

Automatic Crack Detection on Road Pavements Using Encoder-Decoder Architecture

Fan

Chen

et al. 2020

Materials

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Automatic crack detection from images is an important task that is adopted to ensure road safety and durability for Portland cement concrete (PCC) and asphalt concrete (AC) pavement. Pavement failure depends on a number of causes including water intrusion, stress from heavy loads, and all the climate effects. Generally, cracks are the first distress that arises on road surfaces and proper monitoring and maintenance to prevent cracks from spreading or forming is important. Conventional algorithms to identify cracks on road pavements are extremely time-consuming and high cost. Many cracks show complicated topological structures, oil stains, poor continuity, and low contrast, which are difficult for defining crack features. Therefore, the automated crack detection algorithm is a key tool to improve the results. Inspired by the development of deep learning in computer vision and object detection, the proposed algorithm considers an encoder-decoder architecture with hierarchical feature learning and dilated convolution, named U-Hierarchical Dilated Network (U-HDN), to perform crack detection in an end-to-end method. Crack characteristics with multiple context information are automatically able to learn and perform end-to-end crack detection. Then, a multi-dilation module embedded in an encoder-decoder architecture is proposed. The crack features of multiple context sizes can be integrated into the multi-dilation module by dilation convolution with different dilatation rates, which can obtain much more cracks information. Finally, the hierarchical feature learning module is designed to obtain a multi-scale features from the high to low- level convolutional layers, which are integrated to predict pixel-wise crack detection. Some experiments on public crack databases using 118 images were performed and the results were compared with those obtained with other methods on the same images. The results show that the proposed U-HDN method achieves high performance because it can extract and fuse different context sizes and different levels of feature maps than other algorithms.

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Section: Aiglern Dataset Generalizationmentioning

confidence: 99%

Automatic Crack Detection on Road Pavements Using Encoder-Decoder Architecture

Fan

Chen

et al. 2020

Materials

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“…While most neural network methods utilize custom made neural networks, there are papers that build on existing neural networks. For example, the work in [67] partly used a pretrained VGG; the work in [9] utilized YOLOv2 [68]; whereas the works in [7,8] built on U-Net [69]. Neural networks combined with image histograms and other separate feature extraction methods have been applied for these problems as well [70].…”

Section: Source Input Data and Data Collectionmentioning

confidence: 99%

Pavement Distress Detection with Deep Learning Using the Orthoframes Acquired by a Mobile Mapping System

et al. 2019

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The subject matter of this research article is automatic detection of pavement distress on highway roads using computer vision algorithms. Specifically, deep learning convolutional neural network models are employed towards the implementation of the detector. Source data for training the detector come in the form of orthoframes acquired by a mobile mapping system. Compared to our previous work, the orthoframes are generally of better quality, but more importantly, in this work, we introduce a manual preprocessing step: sets of orthoframes are carefully selected for training and manually digitized to ensure adequate performance of the detector. Pretrained convolutional neural networks are then fine-tuned for the problem of pavement distress detection. Corresponding experimental results are provided and analyzed and indicate a successful implementation of the detector.

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“…The surface to inspect is however extensive, making a manual inspection time-intensive and prone to errors. As a result, efforts have been made in the recent years to detect cracks in an automatic manner [4,32,18,20,35,25,22,30,29,3,8].…”

Section: Introductionmentioning

confidence: 99%

“…(a) Crack500 [32] (b) DeepCrack [18] (c) SDNet2018 [20] (d) CrackTree [35] (e) CCIC* [25] (f) Codebrim [22] (g) AigleRN [4] (h) CrackForest [30] Secondly, some methods only classify images [20,22,25] as containing a crack or not. The shapes and widths of the cracks are not provided, although it can be an important information for assessing the cracks' severity.…”

Section: Introductionmentioning

confidence: 99%

An 'All Terrain' Crack Detector Obtained by Deep Learning on Available Databases

Drouyer¹

2020

Image Processing on Line

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We present a general deep learning method for detecting cracks on all sorts of surfaces. For making this method robust to different types of cracks and acquisition procedures, we have trained our method on four datasets-Crack500, DeepCrack, SDNet2018 and CrackForest. We have also labelled a part of the SDNet2018 dataset so that it contains semantic labels, as it originally only proposed crack/non-crack classifications on the image level. To validate our approach, we perform a cross-dataset study where we train the model on a subset of the datasets and test it on another subset. Results of this study show that training the model on these various datasets makes it more robust to new images, outperforming existing classical and deep learning methods. In order to make our method even more robust to different objects, scenes and illuminations, we have also added images from the Flickr website, leading to an important drop in false positives on extra dataset images. The network seems to function well on images not belonging to any of the datasets, and its publication in IPOL will allow users to enrich further training.

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Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection

Cited by 581 publications

References 51 publications

Automatic Crack Detection on Road Pavements Using Encoder-Decoder Architecture

Automatic Crack Detection on Road Pavements Using Encoder-Decoder Architecture

Pavement Distress Detection with Deep Learning Using the Orthoframes Acquired by a Mobile Mapping System

An 'All Terrain' Crack Detector Obtained by Deep Learning on Available Databases

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