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Adaptive rotation attention network for accurate defect detection on magnetic tile surface
Fang Luo1, Yuan Cui2, Xu Wang3
1School of Mechatronics and Automotive Engineering, Qingyuan Polytechnic, Qingyuan 511500, China.
Mathematical Biosciences and Engineering : MBE
|November 3, 2023
Summary
This study introduces an Adaptive Rotation Attention Network (ARA-Net) for detecting random, tiny surface defects on magnetic tiles. The novel network significantly improves defect detection accuracy, aiding in permanent magnet motor production monitoring.
Area of Science:
- Materials Science
- Manufacturing Engineering
- Computer Vision
Background:
- Defect detection on magnetic tile surfaces is crucial for permanent magnet motor production monitoring.
- Challenges include random defect occurrence and tiny defect sizes overwhelmed by background noise.
Purpose of the Study:
- To propose an effective method for detecting surface defects on magnetic tiles.
- To address the challenges of random and tiny defects in magnetic tile surfaces.
Main Methods:
- An Adaptive Rotation Attention Network (ARA-Net) was developed.
- The network incorporates an Adaptive Rotation Convolution (ARC) module to capture random defects using multi-view features.
- A Rotation Region Attention (RAA) module was designed to focus on defect features against complex backgrounds.
Main Results:
- The proposed ARA-Net demonstrated superior performance compared to existing state-of-the-art methods.
- Experiments were conducted on the MTSD3C6K dataset, validating the network's effectiveness.
Conclusions:
- ARA-Net offers a robust solution for magnetic tile surface defect detection.
- The findings contribute to enhanced monitoring in permanent magnet motor manufacturing.
