Deep learning‐based embedded license plate localisation system

Abstract

In this study the authors propose novel neural network architecture for license plate localisation (LPL) based on an inverted residual structure where the shortcut connections are between the linear bottleneck layers. This residual structure is used for feature extraction in a modified single shot detector for object detection, where standard convolutions are replaced with depthwise separable convolutions in classification layers. The proposed deep learning (DL) solution was tested against three popular international research databases and achieves state-of-the-art results, proving that the proposed model is accurate and robust. Across those databases, the proposed model surpasses other recent LPL works, including DL-based methods, in terms of accuracy and speed. The authors show the proposed architecture to have significant speedup and computational efficiency gains over other DL models, and to have fast per-image localisation processing times sufficient for applications deployed on expensive and commodity hardware alike. Using a novel multi-threading video capture with motion detection then inference algorithm, the authors increase computational efficiency and drop fewer frames overall, allowing for increased performance. Repeated tests show that the proposed method is well-suited to real-time and highly accurate LPL, regardless of hardware.