Jinyue Shen

Qingdao University of Science and Technology

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i-10 index

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Yuyi Shao

Qingdao University of Science and Technology

Zhouzhou Zheng

Northwest A&F University

Guangxu Li

Tianjin Polytechnic University

Bin Yu

Qingdao University of Science and Technology

Yan Zhang

University of Jinan

Jie Zhang

Donghua University

Sen Zhang

Huazhong Agricultural University

Chengliang Tian

Qingdao University

Yan Zhang

Purple Mountain Laboratories

Guodong Ding

Shanghai Jiao Tong University

Cooperative Institutions

Qingdao University of Science and Technology

Huazhong University of Science and Technology

Ministry of Natural Resources

Nanjing University

Chinese Academy of Sciences

Guizhou Electric Power Design and Research Institute

Beihang University

Central South University

Shanghai Jiao Tong University

Shanghai Children's Hospital

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A two-stage CNN for automated tire defect inspection in radiographic image

Measurement Science and Technology (2021)

Zhouzhou Zheng Sen Zhang Jinyue Shen Yuyi Shao Yan Zhang

Visual inspection plays a crucial role during the manufacturing of tires which are essential for safe driving. Due to complicated anisotropic multi-texture background and ambiguous defect in it, automated tire defect detection is facing huge challenges and high costs. In this study, a novel two-stage convolutional neural network (CNN) is proposed for tire inspection by combining an optimized YOLOv3 and improved pyramid scene parsing network. Comparative experiments are conducted with the-state-of-the-art to validate the effectiveness and superior performance of the proposed method. The proposed two-stage CNN method achieves an average precision of 91.39%, the defect semantic segmentation achieves a mean intersection over union of 87.86%. The average detection time for a tire is 1.158 s such that the method can be effectively implemented into the industrial workflow. It can also be easily applied to different visual inspection applications.

Visual inspection

Pyramid (geometry)

Automated X-ray inspection

10.1088/1361-6501/ac13f8

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Citations (25)

Staphylococcal scalded skin syndrome in a child

The Lancet Infectious Diseases (2023)

Qinwei Qian Jinyue Shen Angela Vinturache Guodong Ding

Erythema

Desquamation

10.1016/s1473-3099(22)00742-3

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Citations (3)

Tire defect classification using a deep convolutional sparse-coding network

Measurement Science and Technology (2021)

Zhouzhou Zheng Jinyue Shen Yuyi Shao Jie Zhang Chengliang Tian

Abstract Automatic quality control has garnered great interest from academia and industry in recent decades. Tire defect detection and classification are critical research topics for driving safety and improving yields. In this work, a novel deep convolutional sparse-coding network (DCScNet) is built for tire defect classification. In DCScNet, sparse coding is utilized to replace the convolution kernel of a convolutional neural network (CNN) for the extraction of features. Compared with CNN, it requires unsupervised training, reduces the subjectivity of artificially defined labels, and achieves higher classification accuracy. The experimental results validate its superior classification performance, compared with state-of-the-art classification algorithms. The classification accuracy reaches 96.8%, which meets industrial requirements.

Kernel (algebra)

Neural coding

Convolution (computer science)

Convolutional code

10.1088/1361-6501/abddf3

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Citations (27)

Automated Tire Visual Inspection Based on Low Rank Matrix Recovery

Research Square (Research Square) (2020)

Guangxu Li Zhouzhou Zheng Yuyi Shao Jinyue Shen Yan Zhang

Abstract Visual inspection is a challenging and widely employed process in industries. In this work, an automated tire visual inspection system is proposed based on low rank matrix recovery. Deep Network is employed to perform texture segmentation which benefits low rank decomposition in both quality and computational efficiency. We propose a dual optimization method to improve convergence speed and matrix sparsity by incorporating the improvement of the soft-threshold shrinkage operator by the weight matrix M. We investigated how incremental multiplier affects the decomposition accuracy and the convergence speed of the algorithm. On this basis, image blocks were decomposed into low-rank matrix and sparse matrix in which defects were separated. Comparative experiments have been performed on our dataset. Experimental results validate the theoretical analysis. The method is promising in false alarm, robustness and running time based on multi-core processor distributed computing. It can be extended to other real-time industrial applications.

Robustness

Matrix (chemical analysis)

Rank (graph theory)

10.21203/rs.3.rs-109309/v1

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Citations (1)

Automated Tire visual inspection based on low rank matrix recovery

Multimedia Tools and Applications (2023)

Guangxu Li Zhouzhou Zheng Yuyi Shao Jinyue Shen Yan Zhang

Robustness

Matrix (chemical analysis)

Rank (graph theory)

10.1007/s11042-023-14467-1

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Citations (4)

Infrared image impulse noise suppression using tensor robust principal component analysis and truncated total variation

Applied Optics (2021)

Yan Zhang Yuyi Shao Jinyue Shen Yao Lu Zhouzhou Zheng

Infrared image denoising is an essential inverse problem that has been widely applied in many fields. However, when suppressing impulse noise, existing methods lead to blurred object details and loss of image information. Moreover, computational efficiency is another challenge for existing methods when processing infrared images with large resolution. An infrared image impulse-noise-suppression method is introduced based on tensor robust principal component analysis. Specifically, we propose a randomized tensor singular-value thresholding algorithm to solve the tensor kernel norm based on the matrix stochastic singular-value decomposition and tensor singular-value threshold. Combined with the image blocking, it can not only ensure the denoising performance but also greatly improve the algorithm’s efficiency. Finally, truncated total variation is applied to improve the smoothness of the denoised image. Experimental results indicate that the proposed algorithm outperforms the state-of-the-art methods in computational efficiency, denoising effect, and detail feature preservation.

Structure tensor

Impulse noise

Singular value

Kernel (algebra)

10.1364/ao.421081

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Citations (7)

HAMNet: hyperspectral image classification based on hybrid neural network with attention mechanism and multi-scale feature fusion

International Journal of Remote Sensing (2022)

Jinyue Shen Zhouzhou Zheng Yingwei Sun Mengmeng Zhao Yankang Chang

Recently, convolutional neural network (CNN) has made great progress in hyperspectral image (HSI) classification. Considering the problems of high dimensions, limited training samples and intra-class variations of hyperspectral data, there are challenges for traditional pure 2D or 3D deep convolutional neural networks in classifying HSI. Deeper layers bring gradient dispersion, while 3D feature blocks bring a large number of parameters during feature fusion. In this paper, an end-to-end hybrid convolutional neural network is proposed for HSI classification. Firstly, 3D, 2D and 1D convolution modules are applied, respectively, to perform joint feature extraction of spatial and spectral information. Secondly, a new 3D multi-scale feature fusion strategy is proposed to fuse the high-level and low-level features for ensuring the feature sufficiency. Moreover, channel attention mechanism is introduced to avoid feature channel redundancy and strengthen effective features. Comparative experimental results show that the method can receive satisfactory results on public data sets and small-sample learning problem.

Feature (linguistics)

Fuse (electrical)

10.1080/01431161.2022.2109222

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Citations (5)