scholarly journals OverFeat Network Algorithm for Fabric Defect Detection in Textile Industry

2021 ◽  
Vol 3 (4) ◽  
pp. 311-321
Author(s):  
S. Kavitha ◽  
J. Manikandan
Keyword(s):  
Defect Detection ◽  
Textile Industry ◽  
Specific Task ◽  
Training Process ◽  
Fabric Defect Detection ◽  
Network Algorithm ◽  
Automation Systems ◽  
Fabric Defect ◽  
Hole Defects ◽  
Artificial Intelligence Program

Automation of systems emerged since the beginning of 20th century. In the early days, the automation systems were developed with a fixed algorithm to perform some specific task in a repeated manner. Such fixed automation systems are revolutionized in recent days with an artificial intelligence program to take decisions on their own. The motive of the proposed work is to train a textile industry system to automatically detect the defects presence in the generated fabrics. The work utilizes an OverFeat network algorithm for such training process and compares its performances with its earlier version called AlexNet and VGG. The experimental work is conducted with a fabric defect dataset consisting of three class images categorised as horizontal, vertical and hole defects.

Fabric defect detection based on a deep convolutional neural network using a two-stage strategy

2020 ◽  
Vol 91 (1-2) ◽  
pp. 130-142 ◽  
Author(s):  
Xiang Jun ◽  
Jingan Wang ◽  
Jian Zhou ◽  
Shuo Meng ◽  
Ruru Pan ◽  
...  
Keyword(s):  
Defect Detection ◽  
Textile Industry ◽  
Local Area ◽  
Histogram Equalization ◽  
Research Field ◽  
Superior Performance ◽  
Local Defect ◽  
Labor Costs ◽  
Fabric Defect Detection ◽  
Fabric Defect

With the rise of labor costs and the advancement of automation in the textile industry, fabric defect detection has become a hot research field in recent years. We proposed a learning-based framework for automatic detection of fabric defects. Firstly, we use a fixed-size square slider to crop the original image to a certain step and regularity. Then an improved histogram equalization is used to enhance each cropped image. Furthermore, the Inception-V1 model is employed to predict the existence of defects in the local area. Finally, we apply the LeNet-5 model, which plays the role of a voting model, to recognize the type of the defect in the fabric. In brief, the proposed framework mainly consists of two steps, namely local defect prediction and global defect recognition. Experiments on the dataset have demonstrated the superior performance in fabric defect detection.

scholarly journals Fabric Defect Detection Using the Sensitive Plant Segmentation Algorithm

2020 ◽  
Vol 28 (3(141)) ◽  
pp. 84-87
Author(s):  
M. Fathu Nisha ◽  
P. Vasuki ◽  
S. Mohamed Mansoor Roomi
Keyword(s):  
Defect Detection ◽  
Textile Industry ◽  
Segmentation Algorithm ◽  
Second Stage ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Different Types ◽  
Improved Performance ◽  
Two Stages ◽  
Defective Pixel

Fabric quality control and defect detection are playing a crucial role in the textile industry with the development of high customer demand in the fashion market. This work presents fabric defect detection using a sensitive plant segmentation algorithm (SPSA) which, is developed with the sensitive behaviour of the sensitive plant biologically named “mimosa pudica”. This method consists of two stages: The first stage enhances the contrast of the defective fabric image and the second stage segments the fabric defects with the aid of the SPSA. The SPSA proposed was developed for defective pixel identification in non-uniform patterns of fabrics. In this paper, the SPSA was built through checking with devised conditions, correlation and error probability. Every pixel was checked with the algorithm developed to be marked either a defective or non-defective pixel. The SPSA proposed was tested on different types of fabric defect databases, showing a much improved performance over existing methods.

scholarly journals Fabric Defect Detection Using Computer Vision Techniques: A Comprehensive Review

2020 ◽  
Vol 2020 ◽  
pp. 1-24
Author(s):  
Aqsa Rasheed ◽  
Bushra Zafar ◽  
Amina Rasheed ◽  
Nouman Ali ◽  
Muhammad Sajid ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Production Process ◽  
Digital Image Processing ◽  
Defect Detection ◽  
Digital Image ◽  
Textile Industry ◽  
Future Research ◽  
Fabric Defect Detection ◽  
Fabric Defect

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.

scholarly journals EfficientDet for fabric defect detection based on edge computing

2021 ◽  
Vol 16 ◽  
pp. 155892502110083
Author(s):  
Shaojun Song ◽  
Junfeng Jing ◽  
Yanqing Huang ◽  
Mingyang Shi
Keyword(s):  
Cloud Computing ◽  
Defect Detection ◽  
Textile Industry ◽  
Detection Algorithm ◽  
Edge Computing ◽  
Detection Methods ◽  
Inspection System ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Manual Methods

The productivity of textile industry is positively correlated with the efficiency of fabric defect detection. Traditional manual detection methods have gradually been replaced by deep learning algorithms based on cloud computing due to the low accuracy and high cost of manual methods. Nonetheless, these cloud computing-based methods are still suboptimal due to the data transmission latency between the end devices and the cloud. To facilitate defect detection with more efficiency, a low-latency, low power consumption, easy upgrade, and automatical visual inspection system with the help of edge computing are proposed in this work. Firstly, the method uses EfficientDet-D0 as the detection algorithm, integrating the advantages of lightweight and scalable and can suit the resource-constrained edge device. Secondly, we performed data augmentations on five fabric datasets and verified the adaptability of the model in different types of fabrics. Finally, we transplanted the trained model to the edge device NVIDIA Jetson TX2 and optimized the model with TensorRT to make it detection faster. The performance of the proposed method is evaluated in five fabric datasets. The detection speed is up to 22.7 frame per second (FPS) on the edge device Jetson TX2. Compared with the cloud-based method, the response time is reduced by 2.5 times, with the capability of real-time industrial defect detection.

Machine Vision Window Division on Self-Adaptation

2011 ◽  
Vol 460-461 ◽  
pp. 617-620
Author(s):  
Xiu Chen Wang
Keyword(s):  
Image Analysis ◽  
Machine Vision ◽  
Defect Detection ◽  
Actual Situation ◽  
Adaptive Window ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Self Adaptation ◽  
Recognition Efficiency ◽  
Division Model

Aiming at time-consuming and ineffective problem of image window division in fabric defect detection, this paper proposes a new adaptive division method after a large number of experiments. This method can quickly and exactly recognize defect feature. Firstly, a division model on adaptive window is established, secondly, the formula to anticipate generally situation of fabric image is given according to the peaks and valleys change in the model, and methods to calculate the division size and position of adaptive window are given. Finally, we conclude that the algorithm in this paper can quickly and simply select the size and position of window division according to actual situation of different fabric images, and the time of image analysis is shortened and the recognition efficiency is improved.

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