A Single-Target License Plate Detection with Attention
This work addresses license plate detection for embedded systems, but appears incremental as it builds on existing CNN-based methods.
The paper tackles the problem of license plate detection by proposing a method to improve efficiency and deployment on embedded devices, achieving unspecified performance gains over time-consuming and cumbersome existing approaches.
With the development of deep learning, Neural Network is commonly adopted to the License Plate Detection (LPD) task and achieves much better performance and precision, especially CNN-based networks can achieve state of the art RetinaNet[1]. For a single object detection task such as LPD, modified general object detection would be time-consuming, unable to cope with complex scenarios and a cumbersome weights file that is too hard to deploy on the embedded device.