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Updated: Dec 12, 2025

Author Spotlight: Advancing Cardiovascular Imaging - Introducing the Spatially Weighted Calcium Score for Early Disease Detection

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Published on: September 22, 2023

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

Chi-Yi Tsai¹, Hao-Wei Chen¹

¹Department of Electrical and Computer Engineering, TamKang University, New Taipei City 251, Taiwan.

Sensors (Basel, Switzerland)

|August 9, 2020

Summary

A new deep learning model, SurfNetv2, accurately identifies Calcium Silicate Board (CSB) surface defects. This Convolutional Neural Network (CNN) achieves high accuracy and real-time performance for automated defect recognition.

Keywords:

SurfNet calcium silicate boards deep learning supervised end-to-end learning surface defect recognition

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Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope

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Published on: May 28, 2016

Area of Science:

Materials Science
Computer Science
Artificial Intelligence

Background:

Surface defect detection is crucial for quality control in manufacturing.
Existing methods for Calcium Silicate Board (CSB) defect recognition may lack accuracy or real-time capabilities.

Purpose of the Study:

To develop an improved Convolutional Neural Network (CNN) architecture for recognizing surface defects on CSB.
To enhance the accuracy and efficiency of automated visual inspection systems.

Main Methods:

A novel CNN architecture, SurfNetv2, was designed, inspired by SurfNet.
The model utilizes a feature extraction module and a surface defect recognition module.
End-to-end supervised learning was employed using manually captured defect images categorized into crash, dirty, uneven, and normal.

Main Results:

SurfNetv2 achieved high recognition accuracies of 99.90% on a private CSB dataset and 99.75% on the public NEU dataset.
The model demonstrated real-time performance, processing images at approximately 199.38 fps (128x128 pixels).
SurfNetv2 outperformed five state-of-the-art methods in defect recognition.

Conclusions:

The proposed SurfNetv2 demonstrates significant potential for real-time automatic surface defect recognition applications.
The deep learning approach effectively utilizes RGB image information for accurate CSB defect identification.