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 Traditional manual detection methods have gradually been replaced by deep learning algorithms based on cloud computing due to the low accuracy and high cost of manual methods.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.

A fabric defect detection algorithm via context-based local texture saliency analysis

2015 ◽  
Vol 27 (5) ◽  
pp. 738-750 ◽  
Author(s):  
Zhoufeng Liu ◽  
Chunlei Li ◽  
Quanjun Zhao ◽  
Liang Liao ◽  
Yan Dong
Keyword(s):  
Defect Detection ◽  
Texture Features ◽  
Detection Algorithm ◽  
Saliency Map ◽  
Image Texture ◽  
Content Type ◽  
Local Texture ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Saliency Analysis

Purpose – Fabric defect detection plays an important role in textile quality control. The purpose of this paper is to propose a fabric defect detection algorithm via context-based local texture saliency analysis. Design/methodology/approach – In the proposed algorithm, a target image is first divided into blocks, then the Local Binary Pattern (LBP) technique is used to extract the texture features of blocks. Second, for a given image block, several other blocks are randomly chosen for calculating the LBP contrast between a given block and the randomly chosen blocks. Based on the obtained contrast information, a saliency map is produced. Finally, saliency map is segmented by using an optimal threshold, which is obtained by an iterative approach. Findings – The experimental results show that the proposed algorithm, integrating local texture features and global image texture information, can detect texture defects effectively. Originality/value – In this paper, a novel fabric defect detection algorithm via context-based local texture saliency analysis is proposed.

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.

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