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Rethinking and Designing a High-performing Automatic License Plate Recognition Approach
Our study illustrates the outstanding design of ALPR with four insights: (1) the resampling-based cascaded framework is beneficial to both speed and accuracy; (2) the highly efficient license plate recognition should abundant additional character segmentation and recurrent neural network (RNN), but adopt a plain convolutional neural network (CNN); (3) in the case of CNN, taking advantage of vertex information on license plates improves the recognition performance; and (4) the weight-sharing character classifier addresses the lack of training images in small-scale datasets.
When Residual Learning Meets Dense Aggregation: Rethinking the Aggregation of Deep Neural Networks
However, the existing networks usually suffer from either redundancy of convolutional layers or insufficient utilization of parameters.
Convolutional Neural Networks with Dynamic Regularization
That is, the regularization strength is fixed to a predefined schedule, and manual adjustments are required to adapt to various network architectures.
