Pavement Crack Width Measurement Based on Laplace's Equation for Continuity and Unambiguity

This paper presents a convolutional sparse coding (CSC)‐based deep random vector functional link network (CSDRN) for distress classification of road structures. The main contribution of this paper is the introduction of CSC into a feature extraction scheme in the distress classification. CSC can extract visual features representing characteristics of target images because it can successfully estimate optimal convolutional dictionary filters and sparse features as visual features by training from a small number of distress images. The optimal dictionaries trained from distress images have basic components of visual characteristics such as edge and line information of distress images. Furthermore, sparse feature maps estimated on the basis of the dictionaries represent both strength of the basic components and location information of regions having their components, and these maps can represent distress images. That is, sparse feature maps can extract key components from distress images that have diverse visual characteristics. Therefore, CSC‐based feature extraction is effective for training from a limited number of distress images that have diverse visual characteristics. The construction of a novel neural network, CSDRN, by the use of a combination of CSC‐based feature extraction and the DRN classifier, which can also be trained from a small dataset, is shown in this paper. Accurate distress classification is realized via the CSDRN.

Section: Related Workmentioning

confidence: 99%

Convolutional sparse coding‐based deep random vector functional link network for distress classification of road structures

Maeda

Takahashi

Ogawa

et al. 2019

“…Most computer vision‐based methods for damage detection primarily adopt image process techniques (IPTs). IPTs can detect some specific types of structural damage, such as concrete cracks (Abdel‐Qader, Abudayyeh, & Kelly, ; Fujita, Mitani, & Hamamoto, ; Nishikawa, Yoshida, Sugiyama, & Fujino, ), concrete spalling (German, Brilakis, & DesRoches, ), and potholes and cracks in asphalt pavement (Koch & Brilakis, ; Wang, Zhang, Wang, Braham, & Qiu, ; Zhang et al., ). To identify or represent the damages from images, IPTs are based on remarkable assumptions.…”

Section: Introductionmentioning

confidence: 99%

Automatic pixel‐level multiple damage detection of concrete structure using fully convolutional network

Zhao

Zhou

2019

369

158

Deep learning‐based structural damage detection methods overcome the limitation of inferior adaptability caused by extensively varying real‐world situations (e.g., lighting and shadow changes). However, most deep learning‐based methods detect structural damage at the image level and grid‐cell level. To provide pixel‐level detection of multiple damages, a Fully Convolutional Network (FCN)‐based multiple damages detection method for concrete structure is proposed. To realize this method, a database of 2,750 images (with 504 × 376 pixels) including crack, spalling, efflorescence, and hole images in concrete structure is built, and the four damages included in those images are labeled manually. Then, the architecture of the FCN is modified, trained, validated, and tested using this database. A strategy of model‐based transfer learning is used to initialize the parameters of the FCN during the training process. The results show 98.61% pixel accuracy (PA), 91.59% mean pixel accuracy (MPA), 84.53% mean intersection over union (MIoU), and 97.34% frequency weighted intersection over union (FWIoU). Subsequently, the robustness and adaptability of the trained FCN model is tested and the damage is extracted, where damage areas are provided according to a calibrated relation between the ratio (the pixel area and true area of the detected object) and the distance from the smartphone to the concrete surface using a laser range finder. A comparative study is conducted to examine the performance of the proposed FCN‐based approach using a SegNet‐based method. The results show that the proposed method substantiates quite better performance and can indeed detect multiple concrete damages at the pixel level in realistic situations.

“…Studies of crack detection using such techniques have mostly focused on edge detection methods, image processing and pattern recognition concepts (Abdel‐Qader, Abudayyeh, & Kelly, ; Adhikari, Moselhi, & Bagchi, ; Jahanshahi, Masri, Padgett, & Sukhatme, ; Tong, Guo, Ling, & Yin, ; X. Xu & Zhang, ; C. Zhang & Elaksher, ). In addition, IPTs together with Laplacian methods were proposed to measure crack width (W. Wang, Zhang, Wang, Braham, & Qiu, ). However, the results based on these methods rely heavily on manually defined parameters and are prone to environmental effects in real‐world situations, where noise from lighting and distortion can significantly affect the results (Ziou & Tabbone, ).…”

Section: Introductionmentioning

confidence: 99%

Concrete crack detection with handwriting script interferences using faster region‐based convolutional neural network

Deng

Lee

2019

168

The current bridge maintenance practice generally involves manual visual inspection, which is highly subjective and unreliable. A technique that can automatically detect defects, for example, surface cracks, is essential so that early warnings can be triggered to prevent disaster due to structural failure. In this study, to permit automatic identification of concrete cracks, an ad‐hoc faster region‐based convolutional neural network (faster R‐CNN) was applied to contaminated real‐world images taken from concrete bridges with complex backgrounds, including handwriting. A dataset of 5,009 cropped images was generated and labeled for two different objects, cracks and handwriting. The proposed network was then trained and tested using the generated image dataset. Four full‐scale images that contained complex disturbance information were used to assess the performance of the trained network. The results of this study demonstrate that faster R‐CNN can automatically locate crack from raw images, even with the presence of handwriting scripts. For comparative study, the proposed network is also compared with You Only Look Once v2 detection technique.