A Convolutional Neural Network for Segmentation of Background Texture and Defect on Copper Clad Lamination Surface

Sison, Harn; Konghuayrob, Poom; Kaitwanidvilai, Somyot

doi:10.1109/iceast.2018.8434483

Cited by 4 publications

(1 citation statement)

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“…Although the network architecture of the CASAE model is relatively large, it can improve the adaptability of the network to external factors. Sison et al [ 14 ] proposed a copper clad lamination surface defect detection system, which applies the smoothing filters to eliminate noise from the surface image while segmenting the defect region from background texture. The authors created a CNN model to learn the local features of surface defects and background texture.…”

Section: Literature Reviewmentioning

confidence: 99%

SurfNetv2: An Improved Real-Time SurfNet and Its Applications to Defect Recognition of Calcium Silicate Boards

Tsai

Chen

2020

Sensors

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This paper presents an improved Convolutional Neural Network (CNN) architecture to recognize surface defects of the Calcium Silicate Board (CSB) using visual image information based on a deep learning approach. The proposed CNN architecture is inspired by the existing SurfNet architecture and is named SurfNetv2, which comprises a feature extraction module and a surface defect recognition module. The output of the system is the recognized defect category on the surface of the CSB. In the collection of the training dataset, we manually captured the defect images presented on the surface of the CSB samples. Then, we divided these defect images into four categories, which are crash, dirty, uneven, and normal. In the training stage, the proposed SurfNetv2 is trained through an end-to-end supervised learning method, so that the CNN model learns how to recognize surface defects of the CSB only through the RGB image information. Experimental results show that the proposed SurfNetv2 outperforms five state-of-the-art methods and achieves a high recognition accuracy of 99.90% and 99.75% in our private CSB dataset and the public Northeastern University (NEU) dataset, respectively. Moreover, the proposed SurfNetv2 model achieves a real-time computing speed of about 199.38 fps when processing images with a resolution of 128 × 128 pixels. Therefore, the proposed CNN model has great potential for real-time automatic surface defect recognition applications.

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Section: Literature Reviewmentioning

confidence: 99%

SurfNetv2: An Improved Real-Time SurfNet and Its Applications to Defect Recognition of Calcium Silicate Boards

Tsai

Chen

2020

Sensors

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QSurfNet: a hybrid quantum convolutional neural network for surface defect recognition

Mishra

Tsai

2023

Quantum Inf Process

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A lightweight detector based on attention mechanism for aluminum strip surface defect detection

Huang

et al. 2022

Computers in Industry

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A Convolutional Neural Network for Segmentation of Background Texture and Defect on Copper Clad Lamination Surface

Cited by 4 publications

References 12 publications

SurfNetv2: An Improved Real-Time SurfNet and Its Applications to Defect Recognition of Calcium Silicate Boards

SurfNetv2: An Improved Real-Time SurfNet and Its Applications to Defect Recognition of Calcium Silicate Boards

QSurfNet: a hybrid quantum convolutional neural network for surface defect recognition

A lightweight detector based on attention mechanism for aluminum strip surface defect detection

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