Design of Rotation Invariant Model Based on Image Offset Angle and Multibranch Convolutional Neural Networks

ZHANG Meng; LI Xiang; ZHANG Jingwei

doi:10.11999/JEIT240417

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ZHANG Meng, LI Xiang, ZHANG Jingwei. Design of Rotation Invariant Model Based on Image Offset Angle and Multibranch Convolutional Neural Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240417

Citation:

ZHANG Meng, LI Xiang, ZHANG Jingwei. Design of Rotation Invariant Model Based on Image Offset Angle and Multibranch Convolutional Neural Networks[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240417

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Design of Rotation Invariant Model Based on Image Offset Angle and Multibranch Convolutional Neural Networks

doi: 10.11999/JEIT240417

1.
School of Integrated Circuits, Southeast University, Nanjing 211189, China
2.
School of Physical Sciences and Technology, Lanzhou University, Lanzhou 730000, China

Funds: Key-Area Research and Development Program of Guangdong Province (2021B1101270006)

Received Date: 2024-05-29
Rev Recd Date: 2024-11-11

Available Online: 2024-11-18

Abstract

Abstract

Convolutional Neural Networks (CNNs) exhibit translation invariance but lack rotation invariance. In recent years, rotating encoding for CNNs becomes a mainstream approach to address this issue, but it requires a significant number of parameters and computational resources. Given that images are the primary focus of computer vision, a model called Offset Angle and Multibranch CNN (OAMC) is proposed to achieve rotation invariance. Firstly, the model detect the offset angle of the input image and rotate it back accordingly. Secondly, feed the rotated image into a multibranch CNN with no rotation encoding. Finally, Response module is used to output the optimal branch as the final prediction of the model. Notably, with a minimal parameter count of 8k, the model achieves a best classification accuracy of 96.98% on the rotated handwritten numbers dataset. Furthermore, compared to previous research on remote sensing datasets, the model achieves up to 8% improvement in accuracy using only one-third of the parameters of existing models.
- Deep learning,
- Rotated image classification,
- Offset angle,
- Multibranch convolutional neural networks

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References(20)

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