Automatic internal crack detection from a sequence of infrared images with a triple-threshold Canny edge detector

Wang, Gaochao; Tse, Peter W.; Yuan, Maodan

doi:10.1088/1361-6501/aa9857

Cited by 44 publications

(31 citation statements)

References 23 publications

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“…In this paper, we proposed AMSFFR-U-net for automatically segment coal rock fractures, which achieved very good performance. DResBlock of the different dilated ratio (1,2,3) are used in the encoding branch to extract different scale fractures. At the same time, there are residual connections in the DResBlock structure to improve the feature extract ability of the network.…”

Section: Discussionmentioning

confidence: 99%

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Adaptive Multi-Scale Feature Fusion Based Residual U-net for Fracture Segmentation in Coal Rock Images

Lu¹,

Fu²,

Zhang³

et al. 2020

Preprint

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Abstract Accurate segmentation of fractures in coal rock CT images is important for safe production and the development of coalbed methane. However, the coal rock fractures formed through natural geological evolution, which are complex, low contrast and different scales. Furthermore, there is no published data set of coal rock. In this paper, we proposed adaptive multi-scale feature fusion based residual U-uet (AMSFFR-U-uet) for fracture segmentation in coal rock CT images. The dilated residual blocks (DResBlock) with dilated ratio (1,2,3) are embedded into encoding branch of the U-uet structure, which can improve the ability of extract feature of network and capture different scales fractures. Furthermore, feature maps of different sizes in the encoding branch are concatenated by adaptive multi-scale feature fusion (AMSFF) module. And AMSFF can not only capture different scales fractures but also improve the restoration of spatial information. To alleviate the lack of coal rock fractures training data, we applied a set of comprehensive data augmentation operations to increase the diversity of training samples. Our network, U-net and Res-U-net are tested on our test set of coal rock CT images with five different region coal rock samples. The experimental results show that our proposed approach improve the average Dice coefficient by 2.9%, the average precision by 7.2% and the average Recall by 9.1% , respectively. Therefore, AMSFFR-U-net can achieve better segmentation results of coal rock fractures, and has stronger generalization ability and robustness.

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Section: Discussionmentioning

confidence: 99%

“…The main digital image processing techniques based on traditional segmentation methods, such as threshold-based segmentation [1][2], edge detection [3][4], region growing [5] and segmentation based on watershed [6] etc. These traditional methods need to design the hand-craft feature representation for a new target segmentation [7].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Multi-Scale Feature Fusion Based Residual U-net for Fracture Segmentation in Coal Rock Images

Lu¹,

Fu²,

Zhang³

et al. 2020

Preprint

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“…Simply put, the boundary line of the crack region may be unclear, due to the image blurring problem. With this condition, the crack damage detecting models using edge analysis (e.g., Otsu and Canny) failed to detect the whole crack damage regions using the blurry concrete image, as depicted in Figure 7 (7)(8)(9). Compared with the SVM based crack detector, DL and our presented MECD method performed better for coping with the image blur problem.…”

Section: Qualitative Evaluationmentioning

confidence: 94%

“…In addition, similar to the edge-based crack detection methods, an Otsu based algorithm was exploited for segmenting the crack regions from the backgrounds [8]. Based on the Canny detecting results, Wang et al applied the K-means algorithm for exploring the crack regions [9]. Chatterjee et al utilized one adaptive threshold strategy for preliminary crack segmentation, which can remove most of the background content [10].…”

Section: Introductionmentioning

confidence: 99%

Effective Crack Damage Detection Using Multilayer Sparse Feature Representation and Incremental Extreme Learning Machine

Wang

Zhao

et al. 2019

Applied Sciences

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Detecting cracks within reinforced concrete is still a challenging problem, owing to the complex disturbances from the background noise. In this work, we advocate a new concrete crack damage detection model, based upon multilayer sparse feature representation and an incremental extreme learning machine (ELM), which has both favorable feature learning and classification capabilities. Specifically, by cropping and using a sliding window operation and image rotation, a large number of crack and non-crack patches are obtained from the collected concrete images. With the existing image patches, the defect region features can be quickly calculated by the multilayer sparse ELM autoencoder networks. Then, the online incremental ELM classified network is used to recognize the crack defect features. Unlike the commonly-used deep learning-based methods, the presented ELM-based crack detection model can be trained efficiently without tediously fine-tuning the entire-network parameters. Moreover, according to the ELM theory, the proposed crack detector works universally for defect feature extraction and detection. In the experiments, when compared with other recently developed crack detectors, the proposed concrete crack detection model can offer outstanding training efficiency and favorable crack detecting accuracy.

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“…Model-based methods [11] refer to methods that are based on comparing the target image with a model image, which is estimated using a filter. resholding methods [12][13][14] select adaptive threshold values to determine if a pixel is part of a defect region. Zhao et al [15] proposed a method based on the grayscale arranging pairs (GAP) feature for defect detection.…”

Section: Introductionmentioning

confidence: 99%

Blowhole Detection Based on Bidirectional Enhancement and Omnidirectional Analysis for X‐Ray Inspection of Castings

Zhao

Liang

et al. 2019

Mathematical Problems in Engineering

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Small blurred blowholes are difficult to detect, especially those in castings having a complex structure. In this paper, we propose a blowhole detection method based on bidirectional enhancement and omnidirectional analysis for X-ray inspection of castings. After obtaining the X-ray images of a casting from the X-ray inspection equipment, there are two main processes to detect the blowholes. First, in the extraction process for the potential defect region, we propose a method based on bidirectional enhancement that highlights the blurred defects and models the nondefective structure. Second, in the precise defect identification process, we propose a method to distinguish real defects by 3D model fitting-based omnidirectional analysis. The experimental results indicate that the proposed method has a higher performance in blowhole detection than comparable methods.

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Automatic internal crack detection from a sequence of infrared images with a triple-threshold Canny edge detector

Cited by 44 publications

References 23 publications

Adaptive Multi-Scale Feature Fusion Based Residual U-net for Fracture Segmentation in Coal Rock Images

Adaptive Multi-Scale Feature Fusion Based Residual U-net for Fracture Segmentation in Coal Rock Images

Effective Crack Damage Detection Using Multilayer Sparse Feature Representation and Incremental Extreme Learning Machine

Blowhole Detection Based on Bidirectional Enhancement and Omnidirectional Analysis for X‐Ray Inspection of Castings

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