Image Pyramid for Automatic Segmentation of Fabric Defects

Sarkar, Ankita; Padmavathi, S

doi:10.1007/978-3-319-71767-8_49

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…The pyramid-based image process is a popular method for solving basic problems in image analysis or manipulation including data compression and shape analysis [ 28 – 30 ]. This method has recently attracted many scholars in solving various tasks that involve image processing such as automatic segmentation [ 31 ], texture classification [ 32 , 33 ], nonlinear image registration [ 34 ], and remote sensing [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

Classification of Asphalt Pavement Cracks Using Laplacian Pyramid-Based Image Processing and a Hybrid Computational Approach

Hoang

2018

Computational Intelligence and Neuroscience

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To improve the efficiency of the periodic surveys of the asphalt pavement condition, this study puts forward an intelligent method for automating the classification of pavement crack patterns. The new approach relies on image processing techniques and computational intelligence algorithms. The image processing techniques of Laplacian pyramid and projection integral are employed to extract numerical features from digital images. Least squares support vector machine (LSSVM) and Differential Flower Pollination (DFP) are the two computational intelligence algorithms that are employed to construct the crack classification model based on the extracted features. LSSVM is employed for data classification. In addition, the model construction phase of LSSVM requires a proper setting of the regularization and kernel function parameters. This study relies on DFP to fine-tune these two parameters of LSSVM. A dataset consisting of 500 image samples and five class labels of alligator crack, diagonal crack, longitudinal crack, no crack, and transverse crack has been collected to train and verify the established approach. The experimental results show that the Laplacian pyramid is really helpful to enhance the pavement images and reveal the crack patterns. Moreover, the hybridization of LSSVM and DFP, named as DFP-LSSVM, used with the Laplacian pyramid at the level 4 can help us to achieve the highest classification accuracy rate of 93.04%. Thus, the new hybrid approach of DFP-LSSVM is a promising tool to assist transportation agencies in the task of pavement condition surveying.

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Section: Methodsmentioning

confidence: 99%

Classification of Asphalt Pavement Cracks Using Laplacian Pyramid-Based Image Processing and a Hybrid Computational Approach

Hoang

2018

Computational Intelligence and Neuroscience

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“…For example, Reference [6] firstly preprocesses images using convex optimization, then segments wood defects with Otsu segmentation method. Reference [12] segments fabric defects by the Sobel and the watershed algorithm.These traditional methods involve image preprocessing, feature extraction, feature reduction, and classifier selection, which needs the experience of experts. Besides, most surface defects are tiny and very similar to their background, even the inspection for a single type of defect can be very difficult for these traditional methods.…”

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confidence: 99%

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confidence: 99%

A Compact Convolutional Neural Network for Surface Defect Inspection

Huang

Qiu

Wang

et al. 2020

Sensors

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The advent of convolutional neural networks (CNNs) has accelerated the progress of computer vision from many aspects. However, the majority of the existing CNNs heavily rely on expensive GPUs (graphics processing units). to support large computations. Therefore, CNNs have not been widely used to inspect surface defects in the manufacturing field yet. In this paper, we develop a compact CNN-based model that not only achieves high performance on tiny defect inspection but can be run on low-frequency CPUs (central processing units). Our model consists of a light-weight (LW) bottleneck and a decoder. By a pyramid of lightweight kernels, the LW bottleneck provides rich features with less computational cost. The decoder is also built in a lightweight way, which consists of an atrous spatial pyramid pooling (ASPP) and depthwise separable convolution layers. These lightweight designs reduce the redundant weights and computation greatly. We train our models on groups of surface datasets. The model can successfully classify/segment surface defects with an Intel i3-4010U CPU within 30 ms. Our model obtains similar accuracy with MobileNetV2 while only has less than its 1/3 FLOPs (floating-point operations per second) and 1/8 weights. Our experiments indicate CNNs can be compact and hardware-friendly for future applications in the automated surface inspection (ASI). pixels into regions, with the intention to emphasize the regions corresponding to surface defects. For example, Reference [6] firstly preprocesses images using convex optimization, then segments wood defects with Otsu segmentation method. Reference [12] segments fabric defects by the Sobel and the watershed algorithm.These traditional methods involve image preprocessing, feature extraction, feature reduction, and classifier selection, which needs the experience of experts. Besides, most surface defects are tiny and very similar to their background, even the inspection for a single type of defect can be very difficult for these traditional methods. Although traditional algorithms are designed for specific surfaces, effective measures such as recall, precision, and accuracy can barely achieve the standard of industrial applications. This long-existing bottleneck has been restraining the growth of ASI.Convolutional neural networks (CNNs) greatly stimulate the development of ASI [3,13-15] as deep learning methods in computer vision are achieving the state-of-the-art in recent years. As end-to-end solutions, CNNs also simplify the procedures of ASI. For example, a fully convolutional network (FCN) can segment surface defects by supervised learning [16], without any extra pre-process or post-process.A notorious limitation of CNNs is computation. In many cases, expensive GPUs with high computing capabilities are necessary to support large workloads. However, in considering manufacturing cost, most core computing resources of ASI platforms are mainly the low-power CPUs of industrial personal computers (IPC), or even FPGAs (field programmable gate array). To take advantage...

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Review of image segmentation techniques for layup defect detection in the Automated Fiber Placement process

et al. 2021

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The aerospace industry has established the Automated Fiber Placement process as a common technique for manufacturing fibre reinforced components. In this process multiple composite tows are placed simultaneously onto a tool. Currently in such processes manual testing requires often up to 50% of the manufacturing duration. Moreover, the accuracy of quality assurance varies significantly with the inspector in charge. Thus, inspection automation provides an effective way to increase efficiency. However, to achieve a proper inspection performance, the segmentation of layup defects need to be examined. In order to improve such defect detection systems, this paper performs a comprehensive ranking of segmentation techniques. Thus, 29 statistical, spectral and structural algorithms from related work were evaluated based on nine substantial criteria as assessed from literature and process requirements. For reasons of determinism and easy technology transferability without the need of much training data, the development of new Machine Learning algorithms is not part of this paper. Afterwards, seven of the most auspicious algorithms were studied experimentally. Therefore, laser line scan sensor depth maps from fibre placement defects were utilised. Furthermore noisy images were generated and applied for testing algorithm robustness. The test data contained five defect categories with 50 samples per class. It was concluded that Adaptive Thresholding and Cell Wise Standard Deviation Thresholding work best yielding detection accuracies mostly $$> 97$$ > 97 %. Noteworthy is that influenced input data can affect the detection results. Feasible algorithms with sensible parameter settings were able to perform reliable defect segmentation for layed material.

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Image Pyramid for Automatic Segmentation of Fabric Defects

Cited by 3 publications

References 10 publications

Classification of Asphalt Pavement Cracks Using Laplacian Pyramid-Based Image Processing and a Hybrid Computational Approach

Classification of Asphalt Pavement Cracks Using Laplacian Pyramid-Based Image Processing and a Hybrid Computational Approach

A Compact Convolutional Neural Network for Surface Defect Inspection

Review of image segmentation techniques for layup defect detection in the Automated Fiber Placement process

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