An efficient and reliable coarse-to-fine approach for asphalt pavement crack detection

Zhang, Dejin; Li, Qingquan; Chen, Ying; Cao, Min; He, Li; Zhang, Bailing

doi:10.1016/j.imavis.2016.11.018

Cited by 137 publications

(67 citation statements)

References 16 publications

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“…To overcome the drawbacks of human-based crack detection method, many image processing techniques (IPTs) are developed to detect concrete cracks [1][2][3], concrete spalling [4], and potholes and cracks in asphalt pavement [5][6][7]. e IPTs can not only recognize cracks from images [8] but also measure the width and orientation of the recognized cracks [9,10].…”

Section: Introductionmentioning

confidence: 99%

Image‐Based Concrete Crack Detection Using Convolutional Neural Network and Exhaustive Search Technique

Zhao

2019

Advances in Civil Engineering

133

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Crack detection is important for the inspection and evaluation during the maintenance of concrete structures. However, conventional image-based methods need extract crack features using complex image preprocessing techniques, so it can lead to challenges when concrete surface contains various types of noise due to extensively varying real-world situations such as thin cracks, rough surface, shadows, etc. To overcome these challenges, this paper proposes an image-based crack detection method using a deep convolutional neural network (CNN). A CNN is designed through modifying AlexNet and then trained and validated using a built database with 60000 images. Through comparing validation accuracy under different base learning rates, 0.01 was chosen as the best base learning rate with the highest validation accuracy of 99.06%, and its training result is used in the following testing process. The robustness and adaptability of the trained CNN are tested on 205 images with 3120 × 4160 pixel resolutions which were not used for training and validation. The trained CNN is integrated into a smartphone application to mobile more public to detect cracks in practice. The results confirm that the proposed method can indeed detect cracks in images from real concrete surfaces.

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Section: Introductionmentioning

confidence: 99%

Image‐Based Concrete Crack Detection Using Convolutional Neural Network and Exhaustive Search Technique

Zhao

2019

Advances in Civil Engineering

133

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“…Zhang et al [5] adopted four-layer convolutional neural network (CNN) to realize crack detection, with an accuracy of 87%, which needs to be improved. Zhang et al [6] proposed a new region growth algorithm to detect road cracks, which is not suitable for detecting small and scattered cracks in the road. e extended finite element formula (XFEM) combined with the genetic algorithm (GA) has been proven to be effective in detecting structural defects [7][8][9], but this method also has many limitations.…”

Section: Introductionmentioning

confidence: 99%

Improved Crack Detection and Recognition Based on Convolutional Neural Network

Chen

Yadav

Khan

et al. 2019

Modelling and Simulation in Engineering

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Concrete cracks are very serious and potentially dangerous. ere are three obvious limitations existing in the present machine learning methods: low recognition rate, low accuracy, and long time. Improved crack detection based on convolutional neural networks can automatically detect whether an image contains cracks and mark the location of the cracks, which can greatly improve the monitoring efficiency. Experimental results show that the Adam optimization algorithm and batch normalization (BN) algorithm can make the model converge faster and achieve the maximum accuracy of 99.71%.

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“…In order to avoid illumination variations and shadows, the thresholding of the localized area has been applied [57,58]. In [59], automation of the threshold selection was proposed. For more complex cases, advanced image analysis such as Gabor filters [23] have been used.…”

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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An efficient and reliable coarse-to-fine approach for asphalt pavement crack detection

Cited by 137 publications

References 16 publications

Image‐Based Concrete Crack Detection Using Convolutional Neural Network and Exhaustive Search Technique

Image‐Based Concrete Crack Detection Using Convolutional Neural Network and Exhaustive Search Technique

Improved Crack Detection and Recognition Based on Convolutional Neural Network

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

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