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Fanchao Meng

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Proceedings Article | 2 February 2023 Paper

MFA-UNet model research for license plate image segmentation

Fanchao Meng, Gangyang Nan, Zizhen Zhang, Xue Bai

Proceedings Volume 12462, 124621Y (2023) https://doi.org/10.1117/12.2661063

KEYWORDS: Convolution, Image segmentation, Detection and tracking algorithms, Image fusion, Target detection, Image resolution, Image processing, Image processing algorithms and systems

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As an important part of license plate recognition system, research for the license plate detection has made great progress in recent years, it is still affected by complex environments such as weather, distance, angle and brightness. Therefore, a MFA-UNet model is proposed in this paper, which is based on the UNet model structure and combines the multi-scale convolution feature fusion module and the spatial attention mechanism. In the last two layers of the up-sampling stage, the multi-scale dilated convolution feature fusion module is used to cancel the pooling operation, which ensures that the receptive field can be increased without losing the image resolution, and the image features can be enhanced. The attention of the license plate area is increased by introducing a spatial attention mechanism; the learning and training process have been optimized by using the focal loss function. Based the experiments results, accuracy of the model algorithm mentioned in this paper is 4.5% higher than the original UNet in IoU (Intersection over union), and average detection accuracy of the MFA-UNet model on the Chinese City Parking Dataset (CCPD) dataset is 97.8%, which is a great improvement compared with the target detection algorithm.

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