Surface crack detection using deep learning with shallow CNN architecture for enhanced computation

Kim, Bubryur; Yuvaraj, N.; Preethaa, K. R. Sri; Pandian, R.

doi:10.1007/s00521-021-05690-8

Cited by 123 publications

(59 citation statements)

References 54 publications

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“…References [ 27 , 28 ] divided pavement crack images into smaller image blocks by using the road grid or sliding windows, and then used a CNN to recognize whether the image block includes a crack or not. In [ 29 ], the shallow CNN-based architecture for concrete crack detection was proposed. This model enabled the employment of deep learning algorithms using low-power computational devices for a hassle-free monitoring of civil structures.…”

Section: Related Workmentioning

confidence: 99%

Automatic Pixel-Level Pavement Crack Recognition Using a Deep Feature Aggregation Segmentation Network with a scSE Attention Mechanism Module

Qiao

Liu

et al. 2021

Sensors

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Pavement crack detection is essential for safe driving. The traditional manual crack detection method is highly subjective and time-consuming. Hence, an automatic pavement crack detection system is needed to facilitate this progress. However, this is still a challenging task due to the complex topology and large noise interference of crack images. Recently, although deep learning-based technologies have achieved breakthrough progress in crack detection, there are still some challenges, such as large parameters and low detection efficiency. Besides, most deep learning-based crack detection algorithms find it difficult to establish good balance between detection accuracy and detection speed. Inspired by the latest deep learning technology in the field of image processing, this paper proposes a novel crack detection algorithm based on the deep feature aggregation network with the spatial-channel squeeze & excitation (scSE) attention mechanism module, which calls CrackDFANet. Firstly, we cut the collected crack images into 512 × 512 pixel image blocks to establish a crack dataset. Then through iterative optimization on the training and validation sets, we obtained a crack detection model with good robustness. Finally, the CrackDFANet model verified on a total of 3516 images in five datasets with different sizes and containing different noise interferences. Experimental results show that the trained CrackDFANet has strong anti-interference ability, and has better robustness and generalization ability under the interference of light interference, parking line, water stains, plant disturbance, oil stains, and shadow conditions. Furthermore, the CrackDFANet is found to be better than other state-of-the-art algorithms with more accurate detection effect and faster detection speed. Meanwhile, our algorithm model parameters and error rates are significantly reduced.

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Section: Related Workmentioning

confidence: 99%

Automatic Pixel-Level Pavement Crack Recognition Using a Deep Feature Aggregation Segmentation Network with a scSE Attention Mechanism Module

Qiao

Liu

et al. 2021

Sensors

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“…Visual inspection datasets are small scale and are expensive to curate. Therefore, several studies adopt standard architectures-such as VGG16, Inception-v3, LeNet, YOLO, or ResNet50 often pre-trained with IMAGENET representations-for the detection of cracks [31,56,61,75], potholes [39], spalls [68], and multiple other damages including corrosion, seapage, and exposed bars [17,28,72,78]. In Table 1, we provide a non-exhaustive list of recent literature studies that have used transfer learning for concrete damage detection tasks.…”

Section: Damage Detection Using Deep (Transfer) Learningmentioning

confidence: 99%

“…Due to the limitation of small-scale datasets, recent studies for damage detection of bridges, tunnels, and roads have adopted transfer learning as the de facto standard for crack detection [7,31,52], pothole identification [39,68], and related defect classification tasks [67]. These studies exclusively rely on cross-domain transfer learning, where IMAGENET is used as the source dataset (or upstream task), which is then fine-tuned for specific downstream tasks.…”

Section: Introductionmentioning

confidence: 99%

Damage detection using in-domain and cross-domain transfer learning

Bukhsh

Jansen

Saeed

2021

Neural Comput & Applic

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We investigate the capabilities of transfer learning in the area of structural health monitoring. In particular, we are interested in damage detection for concrete structures. Typical image datasets for such problems are relatively small, calling for the transfer of learned representation from a related large-scale dataset. Past efforts of damage detection using images have mainly considered cross-domain transfer learning approaches using pre-trained ImageNet models that are subsequently fine-tuned for the target task. However, there are rising concerns about the generalizability of ImageNet representations for specific target domains, such as for visual inspection and medical imaging. We, therefore, evaluate a combination of in-domain and cross-domain transfer learning strategies for damage detection in bridges. We perform comprehensive comparisons to study the impact of cross-domain and in-domain transfer, with various initialization strategies, using six publicly available visual inspection datasets. The pre-trained models are also evaluated for their ability to cope with the extremely low-data regime. We show that the combination of cross-domain and in-domain transfer persistently shows superior performance specially with tiny datasets. Likewise, we also provide visual explanations of predictive models to enable algorithmic transparency and provide insights to experts about the intrinsic decision logic of typically black-box deep models.

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“…Vision-based deep learning approaches have been utilized in health monitoring of structures for purposes such as crack detection [13][14][15][16], fatigue detection [17], concrete spalling detection [18], and corrosion assessment [19,20]. Although these techniques have been proven to be effective in detecting visible damages, they cannot be used to detect damages hidden in internal parts of structures.…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Network for Damage Identification in Civil Structures

Rastin

Amiri

Darvishan

2021

Shock and Vibration

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In recent years, many efforts have been made to develop efficient deep-learning-based structural health monitoring (SHM) methods. Most of the proposed methods employ supervised algorithms that require data from different damaged states of a structure in order to monitor its health conditions. As such data are not usually available for real civil structures, using supervised algorithms for the health monitoring of these structures might be impracticable. This paper presents a novel two-stage technique based on generative adversarial networks (GANs) for unsupervised SHM and damage identification. In the first stage, a deep convolutional GAN (DCGAN) is used to detect and quantify structural damages; the detected damages are then localized in the second stage using a conditional GAN (CGAN). Raw acceleration signals from a monitored structure are used for this purpose, and the networks are trained by only the intact state data of the structure. The proposed method is validated through applications on the numerical model of a bridge health monitoring (BHM) benchmark structure, an experimental steel structure located at Qatar University, and the full-scale Tianjin Yonghe Bridge.

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Surface crack detection using deep learning with shallow CNN architecture for enhanced computation

Cited by 123 publications

References 54 publications

Automatic Pixel-Level Pavement Crack Recognition Using a Deep Feature Aggregation Segmentation Network with a scSE Attention Mechanism Module

Automatic Pixel-Level Pavement Crack Recognition Using a Deep Feature Aggregation Segmentation Network with a scSE Attention Mechanism Module

Damage detection using in-domain and cross-domain transfer learning

Generative Adversarial Network for Damage Identification in Civil Structures

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