Robust license plate detection in the wild

License Plate Detection (LPD) is the pivotal step for License Plate Recognition. In this work, we explore and customize state-of-the-art detection approaches for exclusively handling the LPD in the wild. In-the-wild LPD considers license plates captured in challenging conditions caused by bad weathers, lighting, traffics, and other factors. As conventional methods failed to handle these inevitable conditions, we explore the latest deep learning based detectors, namely YOLO (You-Only-Look-Once) and its variant YOLO-9000 (referred here as YOLO-2), and customize them for effectively handling the LPD. The prime customizations include modification of the grid size and of the bounding box parameter estimation, and the composition of a more challenging AOLPE (Application-Oriented License Plate Extended) database for performance evaluation. The AOLPE database is an extended version of the AOLP database 1 with additional images taken under extreme but frequently-encountered conditions. As the original YOLO and YOLO-2 are not designed for the LPD, they failed to handle the LPD on the AOLPE without the customizations. This study can be one of the pioneering works that revise state-of-the-art real-time deep networks for handling the LPD. It also serves as a case study for those who wish to customize existing deep networks for detecting specific objects. In addition to a pioneering explorations of deep networks for handling the in-the-wild LPD, our contribution also includes the release of the AOLPE database and evaluation protocol for a novel benchmark for the LPD.