ABSTRACT Automatic license plate recognition (ALPR) systems critically depend on the robust and efficient detection of LPs under unconstrained environmental conditions, including significant viewpoint variations and complex backgrounds. To address these challenges, this paper introduces CAD‐Net, a novel corner‐aware LP detection architecture that combines a computationally efficient ResNet‐18 encoder with an efficient multi‐scale feature decoder for accurate LP corner localization. The decoder aggregates and refines features through group dilated convolutions, coordinate attention, and context gated attention, enabling enhanced focus on semantically salient regions while capturing intricate spatial dependencies. The detected LP corner points enable a polygonal region‐of‐interest alignment strategy for geometric rectification of LP features, which is integrated into an end‐to‐end LP recognition framework named CAR‐Net. Comprehensive experiments demonstrate the efficacy of our method. For LP detection, CAD‐Net attains LP detection rates of 99.9% on CCPD‐Base and 100.0% on AOLP‐RP, with a processing speed of 105 frames per second. For end‐to‐end LP recognition, CAR‐Net achieves state‐of‐the‐art performance on multiple benchmarks, CCPD (98.9%), AOLP‐RP (99.2%), PKUdata (98.5%), CLPD (82.3%), and OpenALPR‐BR (99.1%), while maintaining a real‐time inference speed of 72 frames per second. These results confirm practical viability for deployment in real‐world ALPR systems.
Fan et al. (2026) studied this question.