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

Riid, Andri; Lõuk, Roland; Pihlak, Rene; Tepljakov, Aleksei; Vassiljeva, Kristina

doi:10.3390/app9224829

Cited by 28 publications

(23 citation statements)

References 83 publications

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“…Recently, deep learning was successfully applied to image recognition [ 9 , 10 , 11 ]. On this basis, some methods of crack detection based on convolutional neural networks (CNN) are proposed [ 12 , 13 , 14 ]. Cha et al [ 14 ] proposed a defect detection method based on Faster R-CNN [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

CNN Training with Twenty Samples for Crack Detection via Data Augmentation

Wang¹,

Yang²,

Jiang³

et al. 2020

Sensors

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The excellent generalization ability of deep learning methods, e.g., convolutional neural networks (CNNs), depends on a large amount of training data, which is difficult to obtain in industrial practices. Data augmentation is regarded commonly as an effective strategy to address this problem. In this paper, we attempt to construct a crack detector based on CNN with twenty images via a two-stage data augmentation method. In detail, nine data augmentation methods are compared for crack detection in the model training, respectively. As a result, the rotation method outperforms these methods for augmentation, and by an in-depth exploration of the rotation method, the performance of the detector is further improved. Furthermore, data augmentation is also applied in the inference process to improve the recall of trained models. The identical object has more chances to be detected in the series of augmented images. This trick is essentially a performance–resource trade-off. For more improvement with limited resources, the greedy algorithm is adopted for searching a better combination of data augmentation. The results show that the crack detectors trained on the small dataset are significantly improved via the proposed two-stage data augmentation. Specifically, using 20 images for training, recall in detecting the cracks achieves 96% and Fext(0.8), which is a variant of F-score for crack detection, achieves 91.18%.

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Section: Introductionmentioning

confidence: 99%

CNN Training with Twenty Samples for Crack Detection via Data Augmentation

Wang¹,

Yang²,

Jiang³

et al. 2020

Sensors

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“…The architectures added with residual blocks had been investigated by many researchers (Chu et al, 2019; Riid et al, 2019). It was encouraged that, the residual blocks help to fight against accuracy degradation and vanishing gradient problems and enhance the performance of network.…”

Section: Methodsmentioning

confidence: 99%

Deep Learning based Thermal Crack Detection on Structural Concrete Exposed to Elevated Temperature

Andrushia

Anand

Lublóy

et al. 2021

Advances in Structural Engineering

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Health monitoring of concrete including, detecting defects such as cracking, spalling on fire affected concrete structures plays a vital role in the maintenance of reinforced cement concrete structures. However, this process mostly uses human inspection and relies on subjective knowledge of the inspectors. To overcome this limitation, a deep learning based automatic crack detection method is proposed. Deep learning is a vibrant strategy under computer vision field. The proposed method consists of U-Net architecture with an encoder and decoder framework. It performs pixel wise classification to detect the thermal cracks accurately. Binary Cross Entropy (BCA) based loss function is selected as the evaluation function. Trained U-Net is capable of detecting major thermal cracks and minor thermal cracks under various heating durations. The proposed, U-Net crack detection is a novel method which can be used to detect the thermal cracks developed on fire exposed concrete structures. The proposed method is compared with the other state-of-the-art methods and found to be accurate with 78.12% Intersection over Union (IoU).

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“…Architecture induced with residual connection has been used by multiple researchers [31][32][33][34]. It was proven that residual connection helps to fight the vanishing gradient problem, accuracy degradation [35], and improves neural network performance [31,32,34].…”

Section: Residual Blocksmentioning

confidence: 99%

Improved Pixel-Level Pavement-Defect Segmentation Using a Deep Autoencoder

Augustauskas

Lipnickas

2020

Sensors

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Convolutional neural networks perform impressively in complicated computer-vision image-segmentation tasks. Vision-based systems surpass humans in speed and accuracy in quality inspection tasks. Moreover, the maintenance of big infrastructures, such as roads, bridges, or buildings, is tedious and time-demanding work. In this research, we addressed pavement-quality evaluation by pixelwise defect segmentation using a U-Net deep autoencoder. Additionally, to the original neural network architecture, we utilized residual connections, atrous spatial pyramid pooling with parallel and “Waterfall” connections, and attention gates to perform better defect extraction. The proposed neural network configurations showed a segmentation performance improvement over U-Net with no significant computational overhead. Statistical and visual performance evaluation was taken into consideration for the model comparison. Experiments were conducted on CrackForest, Crack500, GAPs384, and mixed datasets.

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

Cited by 28 publications

References 83 publications

CNN Training with Twenty Samples for Crack Detection via Data Augmentation

CNN Training with Twenty Samples for Crack Detection via Data Augmentation

Deep Learning based Thermal Crack Detection on Structural Concrete Exposed to Elevated Temperature

Improved Pixel-Level Pavement-Defect Segmentation Using a Deep Autoencoder

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