已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整的填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Fabric Defect Segmentation Method Based on Deep Learning

分割 稳健性(进化) 计算机科学 人工智能 卷积神经网络 模式识别(心理学) 图像分割 过程(计算) 人工神经网络 深度学习 数据挖掘 生物化学 基因 操作系统 化学
作者
Yanqing Huang,Junfeng Jing,Zhen Wang
出处
期刊:IEEE Transactions on Instrumentation and Measurement [Institute of Electrical and Electronics Engineers]
卷期号:70: 1-15 被引量:75
标识
DOI:10.1109/tim.2020.3047190
摘要

Fabric defect detection plays an essential role in the textile production process, which was widely applied in the textile industry. For fabric defect detection, many algorithms have been proposed. However, lots of important problems, such as the accuracy of detection, the computational complexity of the algorithm, and data imbalance, still needed to be addressed for application in industrial production. In this article, we propose an efficient convolutional neural network for defect segmentation and detection. The design of this framework significantly alleviates the manual annotation cost of the data set; it only needs few defect samples combined with standard samples to learn the potential feature of defects and obtain the location of defects with high accuracy. The network is divided into two parts: segmentation and decision. First, the fabric data set without training is utilized as the input of the segmentation network. Then, the output of the segmentation network is applied as the raw materials for training the decision network. Finally, a well-trained network is used to obtain the location of defects with high accuracy. The proposed method only demands almost 50 defect samples to get accurate segmentation results and can achieve the requirement of real-time detection with a speed of 25 frames per second (FPS). The experimental results based on a public data set and three self-made fabric data sets show that the proposed method significantly outperforms eight state-of-the-art methods in terms of accuracy and robustness.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
眼睛大的广缘完成签到 ,获得积分10
1秒前
汉堡包应助高大的雁菱采纳,获得10
2秒前
致李峋完成签到 ,获得积分10
3秒前
我要发sci发布了新的文献求助10
4秒前
Cxxxx发布了新的文献求助10
4秒前
科研通AI2S应助zengyiyong采纳,获得10
5秒前
CHEN完成签到 ,获得积分10
6秒前
wanci应助科研通管家采纳,获得10
8秒前
Billy应助科研通管家采纳,获得10
8秒前
8秒前
小蘑菇应助科研通管家采纳,获得10
8秒前
MRBBN应助科研通管家采纳,获得100
8秒前
天天快乐应助科研通管家采纳,获得10
8秒前
嗯哼应助科研通管家采纳,获得10
8秒前
英俊的铭应助科研通管家采纳,获得10
8秒前
Jasper应助科研通管家采纳,获得10
8秒前
Ava应助科研通管家采纳,获得10
8秒前
赘婿应助科研通管家采纳,获得10
8秒前
11秒前
adaadlj;a发布了新的文献求助10
12秒前
13秒前
zolwin7发布了新的文献求助10
13秒前
Diligency完成签到 ,获得积分10
18秒前
21秒前
zolwin7完成签到,获得积分10
21秒前
李浅墨完成签到 ,获得积分10
22秒前
ringo完成签到,获得积分10
22秒前
蜡笔小新完成签到,获得积分10
23秒前
Orange应助自觉的语海采纳,获得10
23秒前
科研通AI2S应助舒适丹雪采纳,获得10
24秒前
25秒前
快乐的千秋完成签到,获得积分10
25秒前
哈比人linling完成签到,获得积分10
25秒前
乐观的饭饭完成签到 ,获得积分10
25秒前
吃了颗糖完成签到,获得积分10
27秒前
30秒前
wait完成签到 ,获得积分10
31秒前
斯皮克完成签到,获得积分10
31秒前
研究生完成签到,获得积分10
34秒前
35秒前
高分求助中
Sustainability in ’Tides Chemistry 2000
Studien zur Ideengeschichte der Gesetzgebung 1000
The ACS Guide to Scholarly Communication 1000
TM 5-855-1(Fundamentals of protective design for conventional weapons) 1000
Handbook of the Mammals of the World – Volume 3: Primates 805
Ethnicities: Media, Health, and Coping 800
Gerard de Lairesse : an artist between stage and studio 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3072420
求助须知:如何正确求助?哪些是违规求助? 2726230
关于积分的说明 7493301
捐赠科研通 2373930
什么是DOI,文献DOI怎么找? 1258827
科研通“疑难数据库(出版商)”最低求助积分说明 610392
版权声明 596967