Deep-learning-based license plate detection method using vehicle region extraction

摘要

A new license plate detection method for challenging environments is proposed. Background clutters are common in road scene images and the detection of license plates (occupying only a small part of an image) is considered as a difficult problem. In order to address this problem, a two-step approach is developed: first vehicle regions are detected and the license plate in each vehicle region is localised. This vehicle region detection based approach provides scale information and limits search ranges in license plate detection, so that one can reliably detect license plate regions. To be precise, the faster region-based convolutional neural network algorithm for the vehicle region detection is adopted and candidates for license plates in each detected region with the hierarchical sampling method are generated. Finally, non-plate candidates are filtered out by training a deep convolutional neural network. The proposed method is evaluated on the Caltech dataset and the method showed a precision of 98.39% and a recall of 96.83%, which outperforms conventional methods.