Corner Enhancement Module Based on Deformable Convolutional Networks and Parallel Ensemble Processing Methods for Distorted License Plate Recognition in Real Environments - Sehun Kim; Seongsoo Cho; Jangyeop Kim; Kwangchul Son (Applied Sciences, 2025)
License plate recognition is a computer vision technology that plays a crucial role in intelligent transportation systems and vehicle management. However, in real-world road environments, recognition accuracy significantly decreases due to distortions caused by various viewing angles. In particular, existing systems exhibit severe performance degradation when processing license plate images captured at steep angles. This paper proposes a new approach to solve the license plate recognition problem in such unconstrained environments. To accurately recognize text on distorted license plates, it is crucial to precisely locate the four corners of the plate and correct the distortion. For this purpose, the proposed system incorporates vehicle and license plate detection based on YOLOv8 and integrates a Corner Enhancement Module (CEM) utilizing a Deformable Convolutional Network (DCN) into the model’s neck to ensure robust feature extraction against geometric transformations. Additionally, the system significantly improves corner detection accuracy through parallel ensemble processing of three license plate images: the original and two aspect ratio-adjusted versions (2:1 and 1.5:1). Furthermore, we verified the system’s versatility in real road environments by implementing a real-time license plate recognition system using Raspberry Pi 4 and a camera module.