An End-to-End Contrastive License Plate Detector

As a unique identity of vehicle, License Plate (LP) facilitates the intelligent transportation in many fields, such as traffic enforcement, intelligent transportation dispatching, etc. Recently, the LP detectors are trained by supervised learning which is directly guided by manual annotations and lacks the use of visual knowledge in image content, limiting the further development of detection performance. Inspired by the contrast and comparison in perception of human beings, a contrastive learning method is introduced into license plate detection task and we propose an end-to-end Contrastive License Plate Detector (CLPD). In CLPD, a special contrastive triad for contrastive learning is designed which aims to decouple the foregrounds and backgrounds. Based on this triad, a contrastive learning branch is introduced into the license plate detection pipeline to prompt the feature expression ability of backbone and extracting more discriminative features for detection. This contrastive learning branch is jointly trained with supervised learning branch for detection and it is only used in training, keeping the efficiency in inference. The experiment results show that the proposed CLPD improves the detection accuracy compared to baselines and other license plate detectors significantly on three datasets. The ablation studies further explore the potential of CLPD. In addition, the proposed CLPD has generalization to improve the performance on different baselines. And the visualization results in latent space verify our proposed CLPD aggregates features tightly and extracts discriminative features effectively.