Automated Road Crack Detection Using Deep Convolutional Neural Networks

Mandal, Vishal; Uong, Lan; Adu-Gyamfi, Yaw

doi:10.1109/bigdata.2018.8622327

Cited by 181 publications

(87 citation statements)

References 9 publications

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“…Training result of best iteration was then used to evaluate the performance of proposed crack detection method, as shown in Figure 8. The overall precision, recall, and F1 score of proposed method are 91.95%, 89.31%, and 90.58%, respectively, which are higher than the pavement crack detection results of Faster R-CNN (Song & Wang, 2019) and YOLOv2 (Mandal et al, 2018).…”

Section: Automated Pavement Crack Detection Resultsmentioning

confidence: 76%

“…Song and Wang (2019) used Faster R-CNN to recognize and locate pavement distresses, including detecting pavement cracks, and got accuracy rate of 90.4% for all pavement distresses. Mandal, Uong, and Adu-Gyamfi (2018) used YOLOv2 to detect pavement cracks, and got the results as precision of 0.8851, recall of 0.8710, and F1 score of 0.8780, which could be further improved. Detection methods are much faster than segmentation methods, which is millisecond compared to second.…”

Section: Introductionmentioning

confidence: 99%

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Automated pavement crack detection and segmentation based on two‐step convolutional neural network

Liu

Yang

Lau

et al. 2020

Computer aided Civil Eng

271

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Cracking is a common pavement distress that would cause further severe problems if not repaired timely, which means that it is important to accurately extract the information of pavement cracks through detection and segmentation. Automated pavement crack detection and segmentation using deep learning are more efficient and accurate than conventional methods, which could be further improved. While many existing studies have utilized deep learning in pavement crack segmentation, which segments cracks from non-crack regions, few studies have taken the exact pavement crack detection into account, which identifies cracks in the images from other objects. A two-step pavement crack detection and segmentation method based on convolutional neural network was proposed in this paper. An automated pavement crack detection algorithm was developed using the modified You Only Look Once 3rd version in the first step. The proposed crack segmentation method in the second step was based on the modified U-Net, whose encoder was replaced with a pre-trained ResNet-34 and the upsample part was added with spatial and channel squeeze and excitation (SCSE) modules. Proposed method combines pavement crack detection and segmentation together, so that the detected cracks from the first step are segmented in the second step to improve the accuracy. A dataset of pavement crack images in different circumstances were also built for the study. The F1 score of proposed crack detection and segmentation methods are 90.58% and 95.75%, respectively, which are higher than other state-of-the-art methods. Compared with existing one-step pavement crack detection or segmentation methods, proposed two-step method showed advantages of accuracy.

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Section: Automated Pavement Crack Detection Resultsmentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Automated pavement crack detection and segmentation based on two‐step convolutional neural network

Liu

Yang

Lau

et al. 2020

Computer aided Civil Eng

271

View full text Add to dashboard Cite

show abstract

“…YOLO reasons globally about the image when making predictions and learns generalizable representations of objects [39]. And it has been proved that YOLO performs well among other existing models, such as SSD or R-CNN in pavement defects recognition [40]. Moreover, YOLOv3 performs best especially in small object detection among the four versions of YOLO [41].…”

Section: Journal Of Advanced Transportationmentioning

confidence: 99%

Dynamic Pavement Distress Image Stitching Based on Fine-Grained Feature Matching

Weng

Liu

et al. 2020

Journal of Advanced Transportation

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Camera-based pavement distress detection plays an important role in pavement maintenance. Duplicate collections for the same distress and multiple overlaps of defects are both practical problems that greatly affect the detection results. In this paper, we propose a fine-grained feature-matching and image-stitching method for pavement distress detection to eliminate duplications and visually demonstrates local pavement distress. The original images are processed through a hierarchical structure, including rough data filtering, feature matching, and image stitching. The original data are firstly filtered based on the global position system (GPS) information, which can avoid full-dataset comparison and improve the calculating efficiency. A scale-invariant feature transform is introduced for feature matching based on the extracted key regions using spectral saliency mapping and bounding boxes. Two parameters: the mean Euclidean distance (MEuD) and the matching rate (MCR) are constructed to identify the duplication between two images. A support vector machine is then applied to determine the threshold of MEuD and MCR. This paper further discusses the correlation between the sampling frequency and the number of detection vehicles. The method provided can effectively solve the problem of duplications in pavement distress detection and enhances the feasibility of multivehicle pavement distress detection based on images.

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“…While most neural network methods utilize custom made neural networks, there are papers that build on existing neural networks. For example, the work in [67] partly used a pretrained VGG; the work in [9] utilized YOLOv2 [68]; whereas the works in [7,8] built on U-Net [69]. Neural networks combined with image histograms and other separate feature extraction methods have been applied for these problems as well [70].…”

Section: Source Input Data and Data Collectionmentioning

confidence: 99%

“…Similarly, the work in [2,10,13] used custom datasets. In addition, the work in [9,11] used a low cost approach of obtaining images using mobile phones.…”

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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Automated Road Crack Detection Using Deep Convolutional Neural Networks

Cited by 181 publications

References 9 publications

Automated pavement crack detection and segmentation based on two‐step convolutional neural network

Automated pavement crack detection and segmentation based on two‐step convolutional neural network

Dynamic Pavement Distress Image Stitching Based on Fine-Grained Feature Matching

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

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