Encoder–decoder network for pixel‐level road crack detection in black‐box images

Bang, Su Sik; Park, Somin; Kim, Hongjo; Kim, Hyoungkwan

doi:10.1111/mice.12440

Cited by 353 publications

(190 citation statements)

References 53 publications

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“…A recently developed technique called recurrent neural network (RNN) was utilized and found to perform faster and better than the previous Crack-Net . Bang, Park, Kim, and Kim (2019) developed a pavement crack detection method at pixel level with a deep convolutional encoder-decoder network. Yang et al (2020) proposed an automated pavement crack detection method Feature Pyramid and Hierarchical Boosting Network (FPHBN), which assimilates contextual information of pavement cracks into low-level characteristics using feature pyramid method.…”

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

270

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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: 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

270

View full text Add to dashboard Cite

show abstract

“…Zhang, Miyamori, Mikami, & Saito, 2019), concrete property estimation (Rafiei, Khushefati, Demirboga, & Adeli, 2017), and vehicle type detection in real traffic data (Molina-Cabello, Luque-Baena, López-Rubio, & Thurnhofer-Hemsi, 2018). For pavement health assessments, several image-based methods (Bang, Park, Kim, & Kim, 2019;Gopalakrishnan, Khaitan, Choudhary, & Agrawal, 2017;H. Maeda, Sekimoto, Seto, Kashiyama, & Omata, 2018;K.…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural networks for pavement roughness assessment using calibration‐free vehicle dynamics

Jeong

Ditzler

2020

Computer aided Civil Eng

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Road roughness is a measure of how uncomfortable a ride is, and provides an important indicator for the needs of roadway maintenance or repavement, which is closely tied to the state and federal budget prioritization. As such, accurate and timely monitoring of deteriorating road conditions and following maintenance are essential to improve the overall ride quality on the road. Various technologies, including vehiclemounted laser profiling systems, have been developed and adopted for road roughness (e.g., IRI-International Roughness Index) measurement; however, their high cost limits their use. While recent advances in smartphone technologies allow us to use their embedded accelerometers for road roughness monitoring, the complicated process of necessary vehicle calibration hinders the widespread use of the technology in the actual practices. In this work, a deep learning IRI estimation method is proposed with the goal of using anonymous (i.e., calibration-free) vehicles and their responses measured by smartphones as road roughness sensors. A state-of-the-art deep learning algorithm (i.e., CNN-convolutional neural network) and multimetric vehicle dynamics data (i.e., accelerometer, gyroscope), possibly measured by drivers' smartphones, are employed for the purpose. Optimized CNN architecture and data configuration have been investigated to achieve the best performance. The efficacy of the proposed method has been numerically validated using real road IRI information (i.e., Speedway, Tucson, AZ), real driving speed profiles, and four different types of vehicle data with associated uncertainties.

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“…First, we selected the best learning rate through training and testing. Then we empirically demonstrate the effectiveness of the proposed U-CliqueNet on the tunnel crack datasets that we set up and compared with these most recent algorithms: FCN [23] proposed by Yang et al, U-net [25] proposed by Liu et al, Bang's SegNet [38] and the MFCD [14] proposed by Li et al In addition, skeleton extraction was carried out for the predicted binary image, next the area, length and width of the crack are calculated.…”

Section: Resultsmentioning

confidence: 99%

Automatic Tunnel Crack Detection Based on U-Net and a Convolutional Neural Network with Alternately Updated Clique

et al. 2020

Sensors

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Regular crack inspection of tunnels is essential to guarantee their safe operation. At present, the manual detection method is time-consuming, subjective and even dangerous, while the automatic detection method is relatively inaccurate. Detecting tunnel cracks is a challenging task since cracks are tiny, and there are many noise patterns in the tunnel images. This study proposes a deep learning algorithm based on U-Net and a convolutional neural network with alternately updated clique (CliqueNet), called U-CliqueNet, to separate cracks from background in the tunnel images. A consumer-grade DSC-WX700 camera (SONY, Wuxi, China) was used to collect 200 original images, then cracks are manually marked and divided into sub-images with a resolution of 496 × 496 pixels. A total of 60,000 sub-images were obtained in the dataset of tunnel cracks, among which 50,000 were used for training and 10,000 were used for testing. The proposed framework conducted training and testing on this dataset, the mean pixel accuracy (MPA), mean intersection over union (MIoU), precision and F1-score are 92.25%, 86.96%, 86.32% and 83.40%, respectively. We compared the U-CliqueNet with fully convolutional networks (FCN), U-net, Encoder–decoder network (SegNet) and the multi-scale fusion crack detection (MFCD) algorithm using hypothesis testing, and it’s proved that the MIoU predicted by U-CliqueNet was significantly higher than that of the other four algorithms. The area, length and mean width of cracks can be calculated, and the relative error between the detected mean crack width and the actual mean crack width ranges from −11.20% to 18.57%. The results show that this framework can be used for fast and accurate crack semantic segmentation of tunnel images.

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Encoder–decoder network for pixel‐level road crack detection in black‐box images

Cited by 353 publications

References 53 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

Convolutional neural networks for pavement roughness assessment using calibration‐free vehicle dynamics

Automatic Tunnel Crack Detection Based on U-Net and a Convolutional Neural Network with Alternately Updated Clique

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