Mobile-Unet: An efficient convolutional neural network for fabric defect detection

Softmax函数 计算机科学 人工智能 分割 深度学习 卷积神经网络 特征(语言学) 模式识别(心理学) 卷积(计算机科学) 编码器 反褶积 钥匙(锁) 人工神经网络 算法 语言学 操作系统 哲学 计算机安全
作者
Junfeng Jing,Zhen Wang,Matthias Rätsch,Huanhuan Zhang
出处
期刊:Textile Research Journal [SAGE]
卷期号:92 (1-2): 30-42 被引量:189
标识
DOI:10.1177/0040517520928604
摘要

Deep learning–based fabric defect detection methods have been widely investigated to improve production efficiency and product quality. Although deep learning–based methods have proved to be powerful tools for classification and segmentation, some key issues remain to be addressed when applied to real applications. Firstly, the actual fabric production conditions of factories necessitate higher real-time performance of methods. Moreover, fabric defects as abnormal samples are very rare compared with normal samples, which results in data imbalance. It makes model training based on deep learning challenging. To solve these problems, an extremely efficient convolutional neural network, Mobile-Unet, is proposed to achieve the end-to-end defect segmentation. The median frequency balancing loss function is used to overcome the challenge of sample imbalance. Additionally, Mobile-Unet introduces depth-wise separable convolution, which dramatically reduces the complexity cost and model size of the network. It comprises two parts: encoder and decoder. The MobileNetV2 feature extractor is used as the encoder, and then five deconvolution layers are added as the decoder. Finally, the softmax layer is used to generate the segmentation mask. The performance of the proposed model has been evaluated by public fabric datasets and self-built fabric datasets. In comparison with other methods, the experimental results demonstrate that segmentation accuracy and detection speed in the proposed method achieve state-of-the-art performance.
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
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
1秒前
喵呜发布了新的文献求助10
1秒前
Ava应助k7采纳,获得10
1秒前
领导范儿应助cc采纳,获得10
1秒前
橘子发布了新的文献求助40
1秒前
1秒前
benben完成签到,获得积分10
2秒前
wjq完成签到,获得积分10
2秒前
2秒前
3秒前
亓亓完成签到 ,获得积分10
3秒前
3秒前
phz发布了新的文献求助10
4秒前
4秒前
Stephen完成签到,获得积分10
4秒前
shengChen完成签到,获得积分10
4秒前
4秒前
怎么睡不醒完成签到 ,获得积分10
4秒前
CipherSage应助沉静的迎荷采纳,获得10
5秒前
彩色铅笔完成签到,获得积分10
5秒前
5秒前
6秒前
6秒前
6秒前
淡定的思松应助通~采纳,获得10
6秒前
ycp完成签到,获得积分10
6秒前
wanci应助cc采纳,获得10
6秒前
泽烺木完成签到,获得积分10
6秒前
duizhang完成签到,获得积分10
6秒前
简单茗发布了新的文献求助10
7秒前
7秒前
DAYTOY应助LJL采纳,获得10
8秒前
qianf完成签到,获得积分10
8秒前
9秒前
9秒前
9秒前
Zn应助ZZZpp采纳,获得10
9秒前
脑洞疼应助喵呜采纳,获得10
10秒前
Monik发布了新的文献求助10
10秒前
花开米兰城完成签到,获得积分10
10秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527849
求助须知:如何正确求助?哪些是违规求助? 3107938
关于积分的说明 9287239
捐赠科研通 2805706
什么是DOI,文献DOI怎么找? 1540033
邀请新用户注册赠送积分活动 716893
科研通“疑难数据库(出版商)”最低求助积分说明 709794