Pixel-Wise Crack Detection Using Deep Local Pattern Predictor for Robot Application

Li, Yundong; Li, Hongguang; Wang, Hongren

doi:10.3390/s18093042

Cited by 53 publications

(25 citation statements)

References 41 publications

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“…There are many metrics used to evaluate the performance of defect detection methods, the most commonly used one is ACC [31]. However, sometimes it cannot accurately reflect the segmentation performance (generally, the defect part of the fabric can only account for 5% of the whole picture, if a model predicts that the whole picture is defect-free, then the ACC of this algorithm is 0.95).…”

Section: Metricsmentioning

confidence: 99%

Pixel-Wise Fabric Defect Detection by CNNs Without Labeled Training Data

Wang

Jing

2020

IEEE Access

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Surface inspection is a necessary process of fabric quality control. However, it remains a challenging task owing to diverse types of defects, various patterns of fabric texture, and application requirements for detection speed. In this paper, a lightweight deep learning model is therefore proposed to complete the segmentation of fabric defects. The input of the model is a fabric image, and the output is a binary image. Generally known, a deep learning model usually needs much data to update the parameters. Still, as an abnormal phenomenon, fabric defects are unpredictable, which makes it impossible to collect a large number of data. Distinct from other models, the proposed method is a supervised network but does not need manually labeled samples for training. A fake sample generator is designed to simulate the defect image, which only needs the defect-free fabric image. The proposed model is trained with fake samples and verified with real samples. The experimental results show that the model trained with false data is useful and achieves high segmentation accuracy on real fabric samples. Besides, a loss function is proposed to deal with the problem of imbalance between the number of background pixels and the number of defective pixels in the fabric image. Comprehensive experiments were performed on representative fabric samples to verify the segmentation accuracy and detection speed of this method. INDEX TERMS Fabric defect; deep learning; image segmentation; defect detection; imbalanced dataset.

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Section: Metricsmentioning

confidence: 99%

Pixel-Wise Fabric Defect Detection by CNNs Without Labeled Training Data

Wang

Jing

2020

IEEE Access

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“…Mundt et al [22] leverage convolutional neural networks to classify defects -cracks being among them -on pictures of bridges. Several methods [18,17,15] use deep learning semantic segmentation methods to segment images into crack / non-crack areas.…”

Section: Related Workmentioning

confidence: 99%

“…Although this task might be easy in simple cases such as wide cracks on white homogeneous walls, it becomes much more difficult when the studied surface is rough or textured, or when the crack is thin and not easily discernible. As classical approaches fail to produce satisfying results, machine learning and deep learning approaches have been proposed in recent years [18,17,15]. However, two major problems subside.…”

Section: Introductionmentioning

confidence: 99%

An 'All Terrain' Crack Detector Obtained by Deep Learning on Available Databases

Drouyer¹

2020

Image Processing on Line

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We present a general deep learning method for detecting cracks on all sorts of surfaces. For making this method robust to different types of cracks and acquisition procedures, we have trained our method on four datasets-Crack500, DeepCrack, SDNet2018 and CrackForest. We have also labelled a part of the SDNet2018 dataset so that it contains semantic labels, as it originally only proposed crack/non-crack classifications on the image level. To validate our approach, we perform a cross-dataset study where we train the model on a subset of the datasets and test it on another subset. Results of this study show that training the model on these various datasets makes it more robust to new images, outperforming existing classical and deep learning methods. In order to make our method even more robust to different objects, scenes and illuminations, we have also added images from the Flickr website, leading to an important drop in false positives on extra dataset images. The network seems to function well on images not belonging to any of the datasets, and its publication in IPOL will allow users to enrich further training.

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“…Though the region containing a crack can be correctly picked up by patch-wise classification methods, the accurate characteristics of a crack, such as the orientation and the width, are difficult to be achieved because a patch contains lots of useless non-crack pixels. To address the issue, Li et al [23] proposed the CNN-based local pattern predictor to efficiently classify each pixel into crack or non-crack by using patches centered on the pixel as context, which achieved pixel-level accuracy. Different from patch-based crack recognition, semantic segmentation methods provide a pixel-level problem-solver.…”

Section: Introductionmentioning

confidence: 99%

Ground Crack Recognition Based on Fully Convolutional Network With Multi-Scale Input

Cheng

Guo

et al. 2020

IEEE Access

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Daily inspection of ground crack over mine goaf is a necessary task for environmental protection and mining safety. However, the traditional manual inspection method is time-consuming, misjudgement-prone and with potential dangers because the goaf normally locates in remote regions and inevitably induces complicated and gullies-cross landform. Therefore, Unmanned Aerial Vehicle(UAV) is adopted to capture aerial images of the ground crack, which provides a convenient way for goaf inspection. Though aerial images display a full view for the region containing cracks, the rugged terrain results in rich noises that have the similar characteristics with cracks in an image, such as shadow, cliff, terrace and so on, which may lead to misclassification of cracks. To overcome the obstacles, an improved semantic segmentation framework called MSI-FCN is proposed in the paper. Firstly, a statistic pre-processing method is employed to remove useless patches before training, with the purpose of decreasing the training complexity. Following that, a multi-scale input method is applied to integrate the patches with the output tensor of each stage based on FCN, which precisely recognizes pixel-level crack. Furthermore, a multi-scale connection module is constructed to select the most important contextual information from the features under multiple scales and help for recovering high-dimension features. In particular, a statistic weighted softmax cross-entropy loss function is presented so as to classify crack pixels more rationally. The experimental results for three groups of images under various environmental conditions indicate that MSI-FCN is superior to the FCN-8, FCD-56 and DeepCrack-GF. In addition, the proposed method also shows high performance on CFD and deepcrack dataset. Thus, multi-scale information embedded in the proposed method can provide more local details of low-level features to high-level stages and improve the recognition performances.

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Pixel-Wise Crack Detection Using Deep Local Pattern Predictor for Robot Application

Cited by 53 publications

References 41 publications

Pixel-Wise Fabric Defect Detection by CNNs Without Labeled Training Data

Pixel-Wise Fabric Defect Detection by CNNs Without Labeled Training Data

An 'All Terrain' Crack Detector Obtained by Deep Learning on Available Databases

Ground Crack Recognition Based on Fully Convolutional Network With Multi-Scale Input

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