Fabric Defect Detection Based on Faster RCNN

Wei, Bing; Hao, Kuangrong; Tang, Xue-song; Ren, Lihong

doi:10.1007/978-3-319-99695-0_6

Cited by 42 publications

(29 citation statements)

References 19 publications

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“…Wei et al [76] proposed a modified faster regional-based convolutional neural network (faster RCNN) that is based on the structure of VGG net. e network is modified to enhance the performance while using the fabric defect benchmark.…”

Section: Deep Learning-based Approachesmentioning

confidence: 99%

“…e proposed research is compared with traditional computer vision approaches, and experimental results show the effectiveness of the proposed faster RCNN model. High training cost is one of the drawbacks of the proposed faster RCNN model [76]. Hu et al [77] proposed unsupervised learning approach based on deep convolutional generative adversarial network (DCGAN) to locate the surface defects for texture.…”

Section: Deep Learning-based Approachesmentioning

confidence: 99%

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Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review

Rasheed

Zafar

Rasheed³

et al. 2020

Mathematical Problems in Engineering

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There are different applications of computer vision and digital image processing in various applied domains and automated production process. In textile industry, fabric defect detection is considered as a challenging task as the quality and the price of any textile product are dependent on the efficiency and effectiveness of the automatic defect detection. Previously, manual human efforts are applied in textile industry to detect the defects in the fabric production process. Lack of concentration, human fatigue, and time consumption are the main drawbacks associated with the manual fabric defect detection process. Applications based on computer vision and digital image processing can address the abovementioned limitations and drawbacks. Since the last two decades, various computer vision-based applications are proposed in various research articles to address these limitations. In this review article, we aim to present a detailed study about various computer vision-based approaches with application in textile industry to detect fabric defects. The proposed study presents a detailed overview of histogram-based approaches, color-based approaches, image segmentation-based approaches, frequency domain operations, texture-based defect detection, sparse feature-based operation, image morphology operations, and recent trends of deep learning. The performance evaluation criteria for automatic fabric defect detection is also presented and discussed. The drawbacks and limitations associated with the existing published research are discussed in detail, and possible future research directions are also mentioned. This research study provides comprehensive details about computer vision and digital image processing applications to detect different types of fabric defects.

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Section: Deep Learning-based Approachesmentioning

confidence: 99%

Section: Deep Learning-based Approachesmentioning

confidence: 99%

Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review

Rasheed

Zafar

Rasheed³

et al. 2020

Mathematical Problems in Engineering

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“…However, deep learning method generally requires a large amount of training data, and there are only few publically available datasets, currently. In order to obtain sufficient training data, [14], [15] use their own fabric datasets(a fabric defect dataset created by an on-loom fabric imaging system [14] and a fabric defect dataset collected by the authors [15]) to train neural networks. On the other hand, [16], [17] use the image blocking method to expand the size of training data and then to train neural networks to identify defect contained in these image blocks, thus locating the defective area.…”

Section: Related Workmentioning

confidence: 99%

Fabric Defect Detection Method Combing Image Pyramid and Direction Template

Xie

Zhang

2019

IEEE Access

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Focusing on the fabric defect detection with periodic-pattern and pure-color texture, an algorithm based on Direction Template and Image Pyramid is proposed. The detection process is divided into two stages: model training and defect localization. During the model training stage, we construct an Image Pyramid for each fabric image that does not contain any defects. Then, Stacked De-noising Convolutional Auto-Encoder (SDCAE) is used for image reconstruction, its training sets are created by randomly extracting image blocks from image pyramid, which makes the feature information of the image block more abundant and the reconstruction effect of the model more remarkable. During the defect localization stage, the image to be detected is divided into a number of blocks, and is reconstructed by using the trained SDCAE model. Then, the candidate defective image blocks are roughly located by using the Structural Similarity Index Measurement after the image reconstruction. Subsequently, direction template is introduced to solve the problem of fabric deformation caused by factors such as fabric production environment and photographic angle. We select the direction template of the images to be detected, filter the candidate defective blocks, and further reduce false detection rate of the proposed algorithm. Furthermore, there is no need to calculate size of periodic-pattern during detection for periodic textured fabric. The algorithm is also suitable for defect detection for pure-color fabrics. The experimental results show that the proposed algorithm can achieve better defect localization accuracy, and receive better results in detection of pure-color fabrics, compared with traditional methods. INDEX TERMS Fabric defect detection, direction template, image pyramid, stack de-noising convolutional auto-encoder, similarity measure.

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“…This approach makes the algorithm additionally end-to-end trainable resulting in high accuracy. Furthermore, this model is well established, and fast in training and is the commonly used building block in many object detection tasks ( 41 , 44 , 45 ). Hence, this model is chosen as a basis for CeCILE.…”

Section: Introductionmentioning

confidence: 99%

CeCILE - An Artificial Intelligence Based Cell-Detection for the Evaluation of Radiation Effects in Eucaryotic Cells

et al. 2021

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The fundamental basis in the development of novel radiotherapy methods is in-vitro cellular studies. To assess different endpoints of cellular reactions to irradiation like proliferation, cell cycle arrest, and cell death, several assays are used in radiobiological research as standard methods. For example, colony forming assay investigates cell survival and Caspase3/7-Sytox assay cell death. The major limitation of these assays is the analysis at a fixed timepoint after irradiation. Thus, not much is known about the reactions before or after the assay is performed. Additionally, these assays need special treatments, which influence cell behavior and health. In this study, a completely new method is proposed to tackle these challenges: A deep-learning algorithm called CeCILE (Cell Classification and In-vitroLifecycle Evaluation), which is used to detect and analyze cells on videos obtained from phase-contrast microscopy. With this method, we can observe and analyze the behavior and the health conditions of single cells over several days after treatment, up to a sample size of 100 cells per image frame. To train CeCILE, we built a dataset by labeling cells on microscopic images and assign class labels to each cell, which define the cell states in the cell cycle. After successful training of CeCILE, we irradiated CHO-K1 cells with 4 Gy protons, imaged them for 2 days by a microscope equipped with a live-cell-imaging set-up, and analyzed the videos by CeCILE and by hand. From analysis, we gained information about cell numbers, cell divisions, and cell deaths over time. We could show that similar results were achieved in the first proof of principle compared with colony forming and Caspase3/7-Sytox assays in this experiment. Therefore, CeCILE has the potential to assess the same endpoints as state-of-the-art assays but gives extra information about the evolution of cell numbers, cell state, and cell cycle. Additionally, CeCILE will be extended to track individual cells and their descendants throughout the whole video to follow the behavior of each cell and the progeny after irradiation. This tracking method is capable to put radiobiologic research to the next level to obtain a better understanding of the cellular reactions to radiation.

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Fabric Defect Detection Based on Faster RCNN

Cited by 42 publications

References 19 publications

Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review

Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review

Fabric Defect Detection Method Combing Image Pyramid and Direction Template

CeCILE - An Artificial Intelligence Based Cell-Detection for the Evaluation of Radiation Effects in Eucaryotic Cells

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