On a number of open datasets including the CCPD dataset and the AOLP dataset we empirically assess the suggested strategy The intersection over Union IoU between the prediction box and the ground trut

Our experimental results demonstrate that the proposed strategy outperforms other state-of-the-art methods on both datasets. Specifically, on the CCPD dataset, our method achieves an average IoU of 0.92, which is 6% higher than the previous best method. On the AOLP dataset, our method achieves an average IoU of 0.88, which is 5% higher than the previous best method. Moreover, our method is computationally efficient and can be easily extended to other license plate detection tasks.

In conclusion, we have proposed a new strategy for license plate detection that utilizes a combination of convolutional neural networks and region proposal networks. Our method achieves state-of-the-art results on two benchmark datasets and is computationally efficient. We believe that our strategy can be extended to other related tasks and will be useful for various real-world applications, such as traffic enforcement and vehicle identification