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.
Keywords: attention mechanism; convolutional neural networks; rotation convolution; surface defect detection.