Convolutional Neural Network-Based Pavement Crack Segmentation Using Pyramid Attention Network

Wang, Wenjun; Su, Chao

doi:10.1109/access.2020.3037667

Cited by 65 publications

(21 citation statements)

References 38 publications

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“…Data augmentation is regarded as an effective strategy to address this problem. Popular crack image augmentation methods include random rotations, flips, and changes in lighting [6], [7], [8]. In [3] the proposed FPHBN crack detector is trained on the training set of the CRACK500 dataset and then is tested on 5 bench-mark datasets, including the CRACK500.…”

Section: A Network Architecturementioning

confidence: 99%

“…The loss function is similar to [3]. Wang et al [8] use pre-trained DenseNet121 as an encoder. A feature pyramid attention module is inserted between the encoder and decoder, which combines low-level and highlevel features.…”

Section: Introductionmentioning

confidence: 99%

“…In the cases where images are carefully labeled by experts, this approach can be considered reasonable. The majority of crack detection publications [1], [6], [8], [12]- [16] consider all predictions to be true positive pixels if a crack pixel is k pixels near to the manually labeled crack pixel. Depending on the research k vary from 2 to 5.…”

Section: Introductionmentioning

confidence: 99%

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DAUNet: Deep Augmented Neural Network for Pavement Crack Segmentation

et al. 2021

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Crack detection and measurement are essential tasks for maintaining and ensuring safety. Accurate crack detection is very challenging because of non-uniform intensity, poor continuity, and irregular patterns of cracks. The complexity of the background and variability in the data acquisition process also complicate the problem. Many approaches to crack detection have been proposed, but the accuracy of the detection leaves much to be desired. The aim of this study is to develop a practical crack detection method for real-time maintenance. We focus on a deep end-to-end and pixel-wise crack segmentation. We propose a lightweight U-Net-based network architecture with emphasis on the learning process. In order to verify the effectiveness of the proposed method, we conduct tests on publicly available pavement crack datasets and compare our model with state-of-the-art crack detection methods. Extensive experiments show that the proposed method effectively detects cracks in a complex environment, and achieves superior performance.

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Section: A Network Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DAUNet: Deep Augmented Neural Network for Pavement Crack Segmentation

et al. 2021

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“…In their approach, CNN was trained using a bio-inspired optimizer, and results showed 99% per-pixel segmentation accuracy. A pyramid-based deep architecture has been devised by Wang et al [23] for the problem of road pavement damage. The proposed pyramid model was evaluated with a dataset of 500 images, and results indicated a segmentation accuracy of 0.6235 in terms of IoU measure.…”

Section: Testing Phasementioning

confidence: 99%

Evolving Pre-Trained CNN Using Two-Layers Optimizer for Road Damage Detection From Drone Images

Samma¹,

Suandi

Ismail

et al. 2021

IEEE Access

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There are numerous pre-trained Convolutional Neural Networks (CNN) introduced in the literature, such as AlexNet, VGG-19, and ResNet. These pre-trained CNN models could be reused and applied to tackle different image recognition problems. Unfortunately, these pre-trained CNN models are complex and have a large number of convolutional filters. To tackle such a complexity challenge, this research aims to evolve a pre-trained VGG-19 using an efficient two-layers optimizer. The proposed optimizer performs filters selection of the last layers of VGG-19 guided by the accuracy of the linear SVM classifier. The proposed approach has three main advantages. Firstly, it adopts a powerful two-layers optimizer that works with a micro swarm population. Secondly, it automatically evolves a lightweight deep model which uses a small number of VGG-19 convolutional filters. Thirdly, It applies the developed model for real-world road damage detection from drone-based images. To evaluate the effectiveness of the proposed approach, a total of 529 images were captured by using a drone-based camera for various road damages. Reported results indicated that the proposed model achieved 96.4% F1-score accuracy with a reduction of VGG-19 filter up to 52%. In addition, the proposed two-layers optimizer was able to outperform several related optimizers such as AOA (

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“…Such structures may be computationally ineffective and cause blurred representation [12], leading to a drop in the prediction accuracy. Recently, the emerging hierarchical structure [13] has been applied to deal with the blurry problem. The potential of this framework in crack detection has been verified in [14].…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Convolutional Neural Network With Feature Preservation and Autotuned Thresholding for Crack Detection

et al. 2021

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Drone imagery is increasingly used in automated inspection for infrastructure surface defects, especially in hazardous or unreachable environments. In machine vision, the key to crack detection rests with robust and accurate algorithms for image processing. To this end, this paper proposes a deep learning approach using hierarchical convolutional neural networks with feature preservation (HCNNFP) and an intercontrast iterative thresholding algorithm for image binarization. First, a set of branch networks is proposed, wherein the output of previous convolutional blocks is half-sizedly concatenated to the current ones to reduce the obscuration in the down-sampling stage taking into account the overall information loss. Next, to extract the feature map generated from the enhanced HCNN, a binary contrast-based autotuned thresholding (CBAT) approach is developed at the post-processing step, where patterns of interest are clustered within the probability map of the identified features. The proposed technique is then applied to identify surface cracks on the surface of roads, bridges or pavements. An extensive comparison with existing techniques is conducted on various datasets and subject to a number of evaluation criteria including the average F-measure (AF β ) introduced here for dynamic quantification of the performance. Experiments on crack images, including those captured by unmanned aerial vehicles inspecting a monorail bridge. The proposed technique outperforms the existing methods on various tested datasets especially for GAPs dataset with an increase of about 1.4% in terms of AF β while the mean percentage error drops by 2.2%. Such performance demonstrates the merits of the proposed HCNNFP architecture for surface defect inspection.INDEX TERMS deep learning, crack detection, hierarchical convolutional neural network, feature preserving, thresholding.

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Convolutional Neural Network-Based Pavement Crack Segmentation Using Pyramid Attention Network

Cited by 65 publications

References 38 publications

DAUNet: Deep Augmented Neural Network for Pavement Crack Segmentation

DAUNet: Deep Augmented Neural Network for Pavement Crack Segmentation

Evolving Pre-Trained CNN Using Two-Layers Optimizer for Road Damage Detection From Drone Images

Hierarchical Convolutional Neural Network With Feature Preservation and Autotuned Thresholding for Crack Detection

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