Texture Analysis Based Damage Detection of Ageing Infrastructural Elements

O’Byrne, Michael L.; Schoefs, Franck; Ghosh, Bidisha; Pakrashi, Vikram

doi:10.1111/j.1467-8667.2012.00790.x

Cited by 105 publications

(59 citation statements)

References 44 publications

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“…The performances of two other techniques, which have previously been proposed in the domain of underwater imaging, are also included in Table 1. These techniques are a hybrid method called regionally enhanced multi-phase segmentation (REMPS) [26] and a texture analysis based segmentation technique [27]. Our approach achieves a mean Intersection over Union (IoU) of 87% and a mean accuracy of 94% when tested on 32.…”

Section: Resultsmentioning

confidence: 94%

Semantic Segmentation of Underwater Imagery Using Deep Networks Trained on Synthetic Imagery

O’Byrne

Pakrashi

Schoefs

et al. 2018

JMSE

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Recent breakthroughs in the computer vision community have led to the emergence of efficient deep learning techniques for end-to-end segmentation of natural scenes. Underwater imaging stands to gain from these advances, however, deep learning methods require large annotated datasets for model training and these are typically unavailable for underwater imaging applications. This paper proposes the use of photorealistic synthetic imagery for training deep models that can be applied to interpret real-world underwater imagery. To demonstrate this concept, we look at the specific problem of biofouling detection on marine structures. A contemporary deep encoder-decoder network, termed SegNet, is trained using 2500 annotated synthetic images of size 960 × 540 pixels. The images were rendered in a virtual underwater environment under a wide variety of conditions and feature biofouling of various size, shape, and colour. Each rendered image has a corresponding ground truth per-pixel label map. Once trained on the synthetic imagery, SegNet is applied to segment new real-world images. The initial segmentation is refined using an iterative support vector machine (SVM) based post-processing algorithm. The proposed approach achieves a mean Intersection over Union (IoU) of 87% and a mean accuracy of 94% when tested on 32 frames extracted from two distinct real-world subsea inspection videos. Inference takes several seconds for a typical image.

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Section: Resultsmentioning

confidence: 94%

Semantic Segmentation of Underwater Imagery Using Deep Networks Trained on Synthetic Imagery

O’Byrne

Pakrashi

Schoefs

et al. 2018

JMSE

Self Cite

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show abstract

“…() extracted pavement crack candidates by multiple directional nonminimum suppression and then devised a linear support vector machine (SVM) classifier to distinguish the crack types; O'Byren et al. () developed a texture‐based solution with a nonlinear SVM model, extending the crack detection from linear to nonlinear domain; Cha et al. () stated a mixed method for specific loosed bolt, combining linear SVM and Hough transforms; Zou et al.…”

Section: Related Researchmentioning

confidence: 99%

Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional Network

Yang

Luo

et al. 2018

Computer aided Civil Eng

618

317

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The spatial characteristics of cracks are significant indicators to assess and evaluate the health of existing buildings and infrastructures. However, the current manual crack description method is time consuming and labor consuming. To improve the efficiency of crack inspection, advanced computer vision‐based techniques have been utilized to detect cracks automatically at image level and grid‐cell level. But existing crack detections are of (high specificity) low generality and inefficient, in terms that conventional approaches are unable to identify and measure diverse cracks concurrently at pixel level. Therefore, this research implements a novel deep learning technique named fully convolutional network (FCN) to address this problem. First, FCN is trained by feeding multiple types of cracks to semantically identify and segment pixel‐wise cracks at different scales. Then, the predicted crack segmentations are represented by single‐pixel width skeletons to quantitatively measure the morphological features of cracks, providing valuable crack indicators for assessment in practice, such as crack topology, crack length, max width, and mean width. To validate the prediction, the predicted segmentations are compared with recent advanced method for crack recognition and ground truth. For crack segmentation, the accuracy, precision, recall, and F1 score are 97.96%, 81.73%, 78.97%, and 79.95%, respectively. For crack length, the relative measurement error varies from −48.03% to 177.79%, meanwhile that ranges from −13.27% to 24.01% for crack width. The results show that FCN is feasible and sufficient for crack identification and measurement. Although the accuracy is not as high as CrackNet because of three types of errors, the prediction has been increased to pixel level and the training time has been dramatically decreased to several per cents of previous methods due to the novel end‐to‐end structure of FCN, which combines typical convolutional neural networks and deconvolutional layers.

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“…O'Byrne et al. () proposed an enhanced texture segmentation technique for structural damage detection and classification based on GLCM and support vector machines (SVMs). Plevris and Asteris () approximated the failure surface of masonry structures under biaxial stress using neural networks.…”

Section: Introductionmentioning

confidence: 99%

Damage Classification for Masonry Historic Structures Using Convolutional Neural Networks Based on Still Images

Wang

Zhao

et al. 2018

Computer aided Civil Eng

121

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Manual inspection (i.e., visual inspection and/or with professional equipment) is the most predominant approach for identifying and assessing superficial damage of masonry historic structures at present. However, this method is costly and at times difficult to apply to remote structures or components. Existing convolutional neural network (CNN)‐based damage detection methods have not been specifically designed for the multiple damage identification of masonry historic structures. To overcome these limits, a deep architecture of CNN damage classification techniques for masonry historic structures is proposed in this article using a sliding window‐based CNN method to identify and locate four categories of damage (intact, crack, efflorescence, and spall) with an accuracy of 94.3%. This is the first attempt to identify the multidamage of historic masonry structures based on CNN techniques and achieve excellent classification results. The data are only trained and tested from images of the Forbidden City Wall in China, and the pixel resolutions of stretcher brick images and header brick images are 480 × 105 and 210 × 105, respectively. Two CNNs (AlexNet and GoogLeNet) are both trained on a small dataset (2,000 images for training, 400 images for validation and testing) and a large dataset (20,000 images for training, 4,000 images for validation and testing). The performance of the trained model (94.3% accuracy) is examined on five new images with 1,860 × 1,260 pixel resolutions.

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Texture Analysis Based Damage Detection of Ageing Infrastructural Elements

Cited by 105 publications

References 44 publications

Semantic Segmentation of Underwater Imagery Using Deep Networks Trained on Synthetic Imagery

Semantic Segmentation of Underwater Imagery Using Deep Networks Trained on Synthetic Imagery

Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional Network

Damage Classification for Masonry Historic Structures Using Convolutional Neural Networks Based on Still Images

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