Adaptive rotation attention network for accurate defect detection on magnetic tile surface

Fang Luo; Yuan Cui; Xu Wang; Zhiliang Zhang; Yong Liao; Fang Luo; Yuan Cui; Xu Wang; Zhiliang Zhang; Yong Liao

doi:10.3934/mbe.2023779

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 9: 17554-17568. doi: 10.3934/mbe.2023779

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Research article

Adaptive rotation attention network for accurate defect detection on magnetic tile surface

1.
School of Mechatronics and Automotive Engineering, Qingyuan Polytechnic, Qingyuan 511500, China
2.
Department of Intelligent Control, Guangzhou Light Industry Vocational School, Guangzhou 510300, China
3.
School of Automation, Guangdong University of Technology, Guangzhou 510006, China
4.
Microelectronics and Optoelectronics Technology Key Laboratory of Hunan Higher Education, School of Physics and Electronic Electrical Engineering, Xiangnan University, Chenzhou 423000, China

Received: 03 July 2023 Revised: 25 August 2023 Accepted: 30 August 2023 Published: 13 September 2023

Defect detection on magnetic tile surfaces is of great significance for the production monitoring of permanent magnet motors. However, it is challenging to detect the surface defects from the magnetic tile due to these issues: 1) Defects appear randomly on the surface of the magnetic tile; 2) the defects are tiny and often overwhelmed by the background. To address such problems, an Adaptive Rotation Attention Network (ARA-Net) is proposed for defect detection on the magnetic tile surface, where the Adaptive Rotation Convolution (ARC) module is devised to capture the random defects on the magnetic tile surface by learning multi-view feature maps, and then the Rotation Region Attention (RAA) module is designed to locate the small defects from the complicated background by focusing more attention on the defect features. Experiments conducted on the MTSD3C6K dataset demonstrate the proposed ARA-Net outperforms the state-of-the-art methods, further providing assistance for permanent magnet motor monitoring.
- surface defect detection,
- rotation convolution,
- attention mechanism,
- convolutional neural networks
Citation: Fang Luo, Yuan Cui, Xu Wang, Zhiliang Zhang, Yong Liao. Adaptive rotation attention network for accurate defect detection on magnetic tile surface[J]. Mathematical Biosciences and Engineering, 2023, 20(9): 17554-17568. doi: 10.3934/mbe.2023779

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Abstract

Defect detection on magnetic tile surfaces is of great significance for the production monitoring of permanent magnet motors. However, it is challenging to detect the surface defects from the magnetic tile due to these issues: 1) Defects appear randomly on the surface of the magnetic tile; 2) the defects are tiny and often overwhelmed by the background. To address such problems, an Adaptive Rotation Attention Network (ARA-Net) is proposed for defect detection on the magnetic tile surface, where the Adaptive Rotation Convolution (ARC) module is devised to capture the random defects on the magnetic tile surface by learning multi-view feature maps, and then the Rotation Region Attention (RAA) module is designed to locate the small defects from the complicated background by focusing more attention on the defect features. Experiments conducted on the MTSD3C6K dataset demonstrate the proposed ARA-Net outperforms the state-of-the-art methods, further providing assistance for permanent magnet motor monitoring.

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