scholarly journals Fabric Defect Detection based on Improved Object as Point

2021 ◽  
Vol 13 (3) ◽  
pp. 1-13
Author(s):  
Yuan He ◽  
Xin-Yue Huang ◽  
Francis Eng Hock Tay
Keyword(s):  
Real Time ◽  
Defect Detection ◽  
Detection Method ◽  
Detection Algorithm ◽  
Error Rates ◽  
Automated Detection ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Low Efficiency

In the field of fabric manufacturing, many factories still utilise the traditional manual detection method. It requires a lot of labour, resulting in high error rates and low efficiency. In this paper, we represent a realtime automated detection method based on object as point. This work makes three attributions. First, we build a fabric defects database and augment the data to training the intelligence model. Second, we provide a real-time fabric defects detection algorithm, which have potential to be applied in manufacturing. Third, we figure out CenterNet with soft NMS will improved the performance in fabric defect detection area, which is considered an NMS-free algorithm. Experiment results indicated that our lightweight network based method can effectively and efficiently detect five different fabric defects.

Real-time Fabric Defect Detection Algorithm Based on S-YOLOV3 Model

2020 ◽  
Vol 57 (16) ◽  
pp. 161001
Author(s):  
周君 Zhou Jun ◽  
景军锋 Jing Junfeng ◽  
张缓缓 Zhang Huanhuan ◽  
王震 Wang Zhen ◽  
黄汉林 Huang Hanlin
Keyword(s):  
Real Time ◽  
Defect Detection ◽  
Detection Algorithm ◽  
Fabric Defect Detection ◽  
Fabric Defect

scholarly journals Real-time fabric defect detection based on multi-scale convolutional neural network

2020 ◽  
Vol 2 (4) ◽  
pp. 189-196
Author(s):  
Shuxuan Zhao ◽  
Li Yin ◽  
Jie Zhang ◽  
Junliang Wang ◽  
Ray Zhong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Defect Detection ◽  
Multi Scale ◽  
Fabric Defect Detection ◽  
Fabric Defect

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.

scholarly journals Automatic Fabric Defect Detection Method Using PRAN-Net

2020 ◽  
Vol 10 (23) ◽  
pp. 8434
Author(s):  
Peiran Peng ◽  
Ying Wang ◽  
Can Hao ◽  
Zhizhong Zhu ◽  
Tong Liu ◽  
...  
Keyword(s):  
Defect Detection ◽  
Detection Method ◽  
Ground Truth ◽  
Inspection Time ◽  
Feature Maps ◽  
Multi Scale ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Feature Pyramid ◽  
Quality Process

Fabric defect detection is very important in the textile quality process. Current deep learning algorithms are not effective in detecting tiny and extreme aspect ratio fabric defects. In this paper, we proposed a strong detection method, Priori Anchor Convolutional Neural Network (PRAN-Net), for fabric defect detection to improve the detection and location accuracy of fabric defects and decrease the inspection time. First, we used Feature Pyramid Network (FPN) by selected multi-scale feature maps to reserve more detailed information of tiny defects. Secondly, we proposed a trick to generate sparse priori anchors based on fabric defects ground truth boxes instead of fixed anchors to locate extreme defects more accurately and efficiently. Finally, a classification network is used to classify and refine the position of the fabric defects. The method was validated on two self-made fabric datasets. Experimental results indicate that our method significantly improved the accuracy and efficiency of detecting fabric defects and is more suitable to the automatic fabric defect detection.

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