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Article
Materials Science, Textiles
Junfeng Jing et al.
Summary: Deep learning-based fabric defect detection methods have great potential in improving production efficiency and product quality. However, challenges such as real-time performance and data imbalance need to be addressed. To overcome these challenges, an efficient convolutional neural network, Mobile-Unet, is proposed. It utilizes the median frequency balancing loss function and depth-wise separable convolution to improve the accuracy and speed of defect segmentation.
TEXTILE RESEARCH JOURNAL
(2022)
Article
Computer Science, Artificial Intelligence
Xinxin Zhou et al.
Summary: This paper proposes a method for human detection in crowded scenarios by using a feature pyramid network (FPN), multi-scale feature fusion technology, and attention mechanisms. The method, called SA-FPN, consists of a Scale-FPN structure, an attention-based lateral connection (ALC) module, and a bottom-up path augmentation (BPA) module. Experimental results show the effectiveness of SA-FPN in improving human detection performance.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Software Engineering
Guohua Liu et al.
Summary: This paper proposes a fabric defect detection method based on low-rank decomposition with structural constraints. The method extracts energy features and constructs a fusion image to highlight defective regions, then builds a new low-rank decomposition model with structured sparsity-inducing norm introduced, and obtain the defect detection result through thresholding the sparse part. Experimental comparisons show the superiority of the proposed method over several state-of-the-art fabric defect detection methods.
Article
Computer Science, Artificial Intelligence
Yun Wu et al.
Summary: This study presents the FPANet model, which uses the novel SeBiFPN and lightweight feature pyramid fusion module, and addresses border segmentation issues through BRM, achieving high-quality real-time semantic segmentation with a better balance of speed and accuracy compared to state-of-the-art methods.
APPLIED INTELLIGENCE
(2022)
Article
Engineering, Multidisciplinary
Shuang Gao et al.
Summary: This work aims to enhance the automation level of industrial pearl classification through deep learning methods. To address the issue of imbalanced datasets, an enhanced generative adversarial network named MVWGAN is proposed to generate high-quality multiview images and balance the datasets. Experimental results demonstrate that the MVWGAN method effectively solves the imbalanced learning problem and improves the classification performance of pearl classification.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Yihao Luo et al.
Summary: This paper proposes a novel channel enhancement feature pyramid network (CE-FPN) to address the issues of channel reduction and aliasing effects in object detection. By introducing sub-pixel skip fusion (SSF) and sub-pixel context enhancement (SCE), stronger feature representations and better feature integration are achieved. Experimental results demonstrate competitive performance of the proposed method, which is also more lightweight compared to state-of-the-art FPN-based detectors.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Wei Wei et al.
Summary: This paper introduces the importance of automatic detection of fabric defects based on machine vision and the use of deep learning image processing technology to train and extract features of target images. By comparing different unsupervised learning algorithms, AE and VAE were chosen to implement the algorithm, and a fabric defect detection system based on VAE was finally implemented on NVIDIA's Jetson TX2.
JOURNAL OF REAL-TIME IMAGE PROCESSING
(2021)
Article
Engineering, Electrical & Electronic
Mina Boluki et al.
Summary: The paper introduces an automatic algorithm based on the optimal Gabor filter for real-time inspection of textile fabrics, incorporating the Cuckoo optimization algorithm and adaptive local binarization method to enhance performance.
SIGNAL IMAGE AND VIDEO PROCESSING
(2021)
Article
Automation & Control Systems
Kunhua Liu et al.
Summary: Researchers proposed a novel generative adversarial network (GAN) for foggy image semantic segmentation, which consists of two parts that aim to extract and express texture, achieving state-of-the-art performance in experiments on foggy cityscapes datasets and foggy driving datasets.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2021)
Article
Computer Science, Artificial Intelligence
Chaofeng Chen et al.
Summary: This paper introduces a novel SPatial Attention Residual Network (SPARNet) built on Face Attention Units (FAUs) for face super-resolution, which effectively extracts key features of facial structures by introducing a spatial attention mechanism. Through quantitative comparisons and the introduction of multi-scale discriminators, the method demonstrates superiority in various metrics and the ability to generate high-resolution images.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Materials Science, Textiles
Guanghua Hu et al.
TEXTILE RESEARCH JOURNAL
(2020)
Article
Geochemistry & Geophysics
Sen Lei et al.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2020)
Article
Computer Science, Hardware & Architecture
Ian Goodfellow et al.
COMMUNICATIONS OF THE ACM
(2020)
Proceedings Paper
Automation & Control Systems
Hong-wei Zhang et al.
PROCEEDINGS OF 2020 IEEE 9TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE (DDCLS'20)
(2020)
Article
Materials Science, Textiles
Junfeng Jing et al.
JOURNAL OF ENGINEERED FIBERS AND FABRICS
(2020)
Article
Materials Science, Textiles
Shengqi Guan
JOURNAL OF THE TEXTILE INSTITUTE
(2018)
Article
Chemistry, Analytical
Shuang Mei et al.
Article
Computer Science, Artificial Intelligence
Shaoqing Ren et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Phillip Isola et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017)
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Gao Huang et al.
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017)
(2017)
