Automatic classification of pavement crack using deep convolutional neural network

Li, Baoxian; Wang, Kelvin C. P.; Zhang, Allen; Yang, En-Hui; Guo-long, Wang

doi:10.1080/10298436.2018.1485917

Cited by 150 publications

(71 citation statements)

References 20 publications

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“…During training, the input 27 × 27 was resized to 5 × 5 as the ground truth. In [51], Li et al proposed a deep CNNs for pavement crack classification based on 3D pavement images, and classify pavement patches cut from 3D images into five categories including the normal category. They trained four supervised CNNs classification models with different sizes of receptive field, and find that different size of receptive field have a slight effect on the classification accuracy.…”

Section: A: Crack Detection Based On Classificationmentioning

confidence: 99%

Review of Pavement Defect Detection Methods

2020

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Road pavement cracks detection has been a hot research topic for quite a long time due to the practical importance of crack detection for road maintenance and traffic safety. Many methods have been proposed to solve this problem. This paper reviews the three major types of methods used in road cracks detection: image processing, machine learning and 3D imaging based methods. Image processing algorithms mainly include threshold segmentation, edge detection and region growing methods, which are used to process images and identify crack features. Crack detection based traditional machine learning methods such as neural network and support vector machine still relies on hand-crafted features using image processing techniques. Deep learning methods have fundamentally changed the way of crack detection and greatly improved the detection performance. In this work, we review and compare the deep learning neural networks proposed in crack detection in three ways, classification based, object detection based and segmentation based. We also cover the performance evaluation metrics and the performance of these methods on commonly-used benchmark datasets. With the maturity of 3D technology, crack detection using 3D data is a new line of research and application. We compare the three types of 3D data representations and study the corresponding performance of the deep neural networks for 3D object detection. Traditional and deep learning based crack detection methods using 3D data are also reviewed in detail. INDEX TERMS Crack detection, image processing, deep learning, 3D imaging.

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Section: A: Crack Detection Based On Classificationmentioning

confidence: 99%

Review of Pavement Defect Detection Methods

2020

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“…The experimental results demonstrate that all the proposed CNNs can perform the classification with a high accuracy. Overall classification accuracy of each proposed CNN is above 94% [40].Compared with traditional image processing methods, deep learning is more efficient, faster in calculation, and more accurate in recognition. It will also have better results for the defects to be identified in this topic.In the field of target detection, the current Faster RCNN (faster region-based convolutional neural networks) model, Yolo (you only look once) model [6] and SSD (single shot multiBox detector) [7] models all have the advantages of high accuracy and fast speed [8].Unlike the Faster RCNN model, which uses "two-step detection" approach, the Yolo model uses a regression mechanism, so as long as the entire graph is entered, the target can be detected.…”

Section: Introductionmentioning

confidence: 94%

Machine Vision Inspection of Electrical Connectors Based on Improved Yolo v3

2020

IEEE Access

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Aiming at the problems of electrical connector defect detection, such as low automation, low detection accuracy, slow detection speed, and poor robustness, an improved Yolo v3 algorithm was proposed in this paper to detect electrical connector defects. First, the K-means clustering algorithm is used to perform cluster analysis on the data set of this paper to obtain three kinds of candidate frames with aspect ratios, aiming at improving the detection accuracy for the defective objects in this paper; the 8-fold downsampled feature map outputted by the third residual block of the backbone network is upsampled 4 times, and the obtained feature map is merged with the 2-fold downsampled feature map outputted by the second residual block to obtain the fusion feature detection layer; the 6 DBL units passed by the target detection layer are changed to 2 DBL unit plus 2 residual units to improve feature reuse and acquisition; In addition, single-scale feature maps are chose for target detection in this paper instead of multi-scale prediction of the original network, which not only saves the calculation amount, but also avoids false detection to a certain extent.;A new detection method is proposed for relative rotation defects between the inner ring area and the outer ring area of the electrical connector.The qualitative and quantitative experimental results show that the improved Yolo v3 algorithm in this paper has better performance and speed for defect detection of various types of electrical connectors, with an accuracy rate of 93.5%, which is more accurate than Faster R-CNN. The original Yolo v3 is faster and basically meets the requirements of the industrial field for electrical connector testing.

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“…Soloviev et al [115], Li et al [116], Tong et al [117], and Fan et al [118] demonstrated the use of DCNNs to detect and recognize cracks as defects with quantifiable properties in applications for crack detection on pavement surfaces (e.g., crack length and size). Fan et al [119] proposed a CNN-based multi-label classifier by improving the positive-to-negative ratio of samples.…”

Section: Crack Detection Through Vision-based DLmentioning

confidence: 99%

Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Azimi

Eslamlou

Pekcan

2020

Sensors

380

158

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Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

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Automatic classification of pavement crack using deep convolutional neural network

Cited by 150 publications

References 20 publications

Review of Pavement Defect Detection Methods

Review of Pavement Defect Detection Methods

Machine Vision Inspection of Electrical Connectors Based on Improved Yolo v3

Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

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