In recent years,license plate recognition system(LPRS)is widely used in various places.Fast and accurate license plate detection is the first and critical step in LPRS.In order to improve the performance of license pl...In recent years,license plate recognition system(LPRS)is widely used in various places.Fast and accurate license plate detection is the first and critical step in LPRS.In order to improve the performance of license plate detection in complex environment,we propose a novel attention U-net with multilevel fusion(AUMF).At first,input images are fed to the network.Then,the feature maps of each level are generated by convolution operations of the original images.Before the feature connection,there are multi-layer splicing and convolution to detect more features.The attention mechanisms are used to retain the information of important regions.In order to ensure that the size of the input and output images are the same,down-sampling and up-sampling are employed to resize the feature mappings between the upper and lower levels.In the complex environment,the AUMF can accurately detect the license plate.To validate the effectiveness of the proposed method,we conducted a series of experiments on the AOLP dataset.The experimental results show that our approach effectively improves the performance of license plate detection under the three different license plate environments of AOLP dataset.展开更多
基金Supported by the National Natural Science Foundation of China(62006150)Shanghai Young Science and Technology Talents Sailing Program(19YF1418400)+1 种基金Shanghai Key Laboratory of Multidimensional Information Processing(2020MIP001)Fundamental Research Funds for the Central Universities
文摘In recent years,license plate recognition system(LPRS)is widely used in various places.Fast and accurate license plate detection is the first and critical step in LPRS.In order to improve the performance of license plate detection in complex environment,we propose a novel attention U-net with multilevel fusion(AUMF).At first,input images are fed to the network.Then,the feature maps of each level are generated by convolution operations of the original images.Before the feature connection,there are multi-layer splicing and convolution to detect more features.The attention mechanisms are used to retain the information of important regions.In order to ensure that the size of the input and output images are the same,down-sampling and up-sampling are employed to resize the feature mappings between the upper and lower levels.In the complex environment,the AUMF can accurately detect the license plate.To validate the effectiveness of the proposed method,we conducted a series of experiments on the AOLP dataset.The experimental results show that our approach effectively improves the performance of license plate detection under the three different license plate environments of AOLP dataset.
