Toward Reliable License Plate Detection in Varied Contexts: Overcoming the Issue of Undersized Plate Annotations

License plate detection and recognition (LPDR) is of paramount importance in the Intelligent Transportation Systems. Most existing license plate (LP) detectors rely on anchors, rendering them vulnerable to multi-scale LPs, especially those of smaller scales, which limited the overall performance of LPDR. Another issue prevalent in LP datasets arises from the inherent ambiguity in manual labeling standards. Owing to this uncertainty, certain small-scale LPs that are distinctly detectable often suffer from annotation omissions. The presence of such noisy data has a detrimental impact on the training of LP detectors. In this paper, we propose ALPD, an anchor-free LP detector along with three key designs, namely the Multi-To-One scale-fusion block (MTO) for cross-scale feature integration, the Multi-Domain Feature Simulation (MDFS) for narrowing the disparities across multiple domains even the unseen ones, and the decoupled heads for better optimizing classification and regression tasks. Besides, ALPD incorporates a semi-supervised training framework using an abstention strategy known as arbitration, wherein a Teacher model and a Student model are trained collaboratively, enabling the supplementation of missing annotations for small license plates. Furthermore, it possesses immunity to model performance degradation when fed with massive quantities of unlabeled or even mislabeled data. The arbitration method along with a penalty factor can effectively guarantee the pseudo-label quality and balance complexity between the Teacher and Student tasks, thus preventing the Student from being constrained by ambiguous pseudo-labels. ALPD outperforms previous state-of-the-art methods on two widely recognized benchmarks and exhibits its robustness and generalizability on the All-round CCPD dataset.