Machine Learning for Crack Detection: Review and Model Performance Comparison

Hsieh, Yung-An; Tsai, Yichang

doi:10.1061/(asce)cp.1943-5487.0000918

Cited by 197 publications

(79 citation statements)

References 79 publications

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“…CNNs can automatically conduct feature mining and prediction using an end-to-end neural network without researchers' need to analyze direct relationships. For example, Adeli and Yeh (1989) Hsieh and Tsai (2020) comprehensively summarized the application of existing deep learning and machine learning methods in pavement distress detection. The overall trend in future research is also discussed, which recommended that the reduction of false-positive rate was the future focus.…”

Section: F I G U R E 1 Different Image Sources For Pavement Distress Detectionmentioning

confidence: 99%

Cross‐scene pavement distress detection by a novel transfer learning framework

Che

Liu

et al. 2021

Computer aided Civil Eng

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Deep learning has achieved promising results in pavement distress detection. However, the training model's effectiveness varies according to the data and scenarios acquired by different camera types and their installation positions. It is time consuming and labor intensive to recollect labeled data and retrain a new model every time the scene changes. In this paper, we propose a transfer learning pipeline to address this problem, which enables a distress detection model to be applied to other untrained scenarios. The framework consists of two main components: data transfer and model transfer. The former trains a generative adversarial network to transfer existing image data into a new scene style. Then, attentive CutMix and image melding are applied to insert distress annotations to synthesize the new scene's labeled data. After data expansion, the latter step transfers the feature extracted by the existing model to the detection application of the new scene through domain adaptation. The effects of varying degrees of knowledge transfer are also discussed. The proposed method is evaluated on two data sets from two different scenes with more than 40,000 images totally. This method can reduce the demand for training data by at least 25% when the model is applied in a new scene. With the same number of training images, the proposed method can improve the model accuracy by 26.55%.

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Section: F I G U R E 1 Different Image Sources For Pavement Distress Detectionmentioning

confidence: 99%

Cross‐scene pavement distress detection by a novel transfer learning framework

Che

Liu

et al. 2021

Computer aided Civil Eng

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show abstract

“…Due to rapid developments in the field of deep learning in the last ten years, several representative algorithms have been proposed. Because of its ability with regard to feature learning and feature expression, deep learning has gradually replaced machine learning algorithms as the mainstream method in the crack detection field [17].…”

Section: Related Workmentioning

confidence: 99%

A Real-Time Bridge Crack Detection Method Based on an Improved Inception-Resnet-v2 Structure

Wang

Shao

et al. 2021

IEEE Access

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Bridge crack detection is essential to ensure bridge safety. The introduction of deep learning technology has made it possible to detect bridge cracks automatically and accurately. In this study, the Inception-Resnet-v2 algorithm was systematically improved and applied to the real-time detection of bridge cracks. We propose an end-to-end bridge crack detection model based on a convolutional neural network. This model combines the advantages of Inception convolution and residual networks, broadening the network width and alleviating the training problem of the deep network. The calculation speed is improved while still ensuring accuracy. Multi-scale feature fusion enables the network to extract contextual information of different scales, which improves the accuracy of crack recognition. The GKA (K-means clustering method based on a genetic algorithm) realizes the accurate segmentation of the target area, greatly enhances the clustering effect, and effectively improves the detection speed. In this model, large fracture datasets are used for training and testing without pre-training. The experimental results show that the performance of this method was improved in all aspects: accuracy, 99.24%; recall, 99.03%; F-measure, 98.79%; and FPS(Frames Per Second), 196. INDEX TERMS bridge crack detection, Inception-Resnet-v2, multiscale feature fusion, GKAThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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“…The trained SegNet model can segment crack images in any size image with the help of sliding window scanning technology. Reference [ 36 ] showed that deeper backbone networks in FCN models and skip connections in U-Net both improved the performance of the crack detection. Li et al combined generative adversarial network (GAN) with fully convolutional DenseNet (FC-DenseNet), and proposed a pavement crack detection method based on adversarial learning semi-supervised semantic segmentation.…”

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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Machine Learning for Crack Detection: Review and Model Performance Comparison

Cited by 197 publications

References 79 publications

Cross‐scene pavement distress detection by a novel transfer learning framework

Cross‐scene pavement distress detection by a novel transfer learning framework

A Real-Time Bridge Crack Detection Method Based on an Improved Inception-Resnet-v2 Structure

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

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