University of Oulu

Liu, W.; Zhang, J.; Su, Z.; Zhou, Z.; Liu, L. Binary Neural Network for Automated Visual Surface Defect Detection. Sensors 2021, 21, 6868. https://doi.org/10.3390/s21206868

Binary neural network for automated visual surface defect detection

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Author: Liu, Wenzhe1; Zhang, Jiehua2; Su, Zhuo2;
Organizations: 1College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
2Center for Machine Vision and Signal Analysis, University of Oulu, 90570 Oulu, Finland
3School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.7 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021110453726
Language: English
Published: Multidisciplinary Digital Publishing Institute, 2021
Publish Date: 2021-11-04
Description:

Abstract

As is well-known, defects precisely affect the lives and functions of the machines in which they occur, and even cause potentially catastrophic casualties. Therefore, quality assessment before mounting is an indispensable requirement for factories. Apart from the recognition accuracy, current networks suffer from excessive computing complexity, making it of great difficulty to deploy in the manufacturing process. To address these issues, this paper introduces binary networks into the area of surface defect detection for the first time, for the reason that binary networks prohibitively constrain weight and activation to +1 and −1. The proposed Bi-ShuffleNet and U-BiNet utilize binary convolution layers and activations in low bitwidth, in order to reach comparable performances while incurring much less computational cost. Extensive experiments are conducted on real-life NEU and Magnetic Tile datasets, revealing the least OPs required and little accuracy decline. When classifying the defects, Bi-ShuffleNet yields comparable results to counterpart networks, with at least 2× inference complexity reduction. Defect segmentation results indicate similar observations. Some network design rules in defect detection and binary networks are also summarized in this paper.

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Series: Sensors
ISSN: 1424-8220
ISSN-E: 1424-8220
ISSN-L: 1424-8220
Volume: 20
Issue: 21
Article number: 6868
DOI: 10.3390/s21206868
OADOI: https://oadoi.org/10.3390/s21206868
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: The research was supported by the National Natural Science Foundation of China under Grant 61872379.
Copyright information: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
  https://creativecommons.org/licenses/by/4.0/