## 3 Proposed Method
### Test-Time Augmentation-Based Parallel Ensemble Processing Technique  
In this study, we propose a test-time augmentation-based aspect ratio adjustment parallel ensemble processing technique to accurately detect corner information from severely distorted license plates. While license plates typically have a rectangular shape, they become distorted in various ways depending on the camera angle. In particular, the larger the angle between the camera imaging plane and the license plate plane, the more significantly the geometric shape and aspect ratio of the license plate become distorted, which directly affects the accuracy of corner detection. Since detecting accurate corners in severely distorted license plate images is very challenging, this study proposes a method that performs corner detection after adjusting the aspect ratio. This allows corner detection algorithms to work more effectively by correcting the geometric characteristics of license plates that have been transformed due to perspective distortion. The proposed technique achieves robustness against distortions at various angles by performing parallel corner detection on license plate images transformed into different aspect ratios. The theoretical background of this aspect ratio transformation is based on the perspective projection model, utilizing the principle of reversing and correcting the distortion that occurs when a planar object in 3D space is projected onto a 2D image. The details of the parallel ensemble processing algorithm can be found in Figure 4.  
The parallel ensemble processing algorithm applies three aspect ratio transformations: The original ratio effectively uses the license plate image as-is for cases with minimal distortion. The 2:1 ratio transformation is effective for moderate angle distortions (approximately 30-60 degrees). When a license plate is photographed from the side, the horizontal length is relatively reduced due to perspective effects, which this transformation partially corrects. The 1.5:1 ratio transformation is effective for severe angle distortions (approximately 60-80 degrees). When a license plate is photographed from an extreme side angle, the perspective effect becomes more severe, significantly reducing the horizontal length. As adjusting to a 2:1 ratio would cause extreme pixel degradation, the 1.5:1 ratio transformation corrects the extreme perspective effect. Figure 3: CEM Applied Neck Structure of Corner Detection. Each transformation is designed to modify only the aspect ratio while preserving the image content, enabling effective corner detection of license plates captured from various angles. This parallel ensemble processing enables stable corner detection under a wider range of distortion conditions compared to existing methods that use only a single aspect ratio transformation. Next, the same corner detection network is applied in parallel to the three transformed images. The ResNet 50-based corner detection network includes a DCN-based CEM and predicts the four corner coordinates of the license plate in each image. After the three aspect ratio transformations (2:1, original, 1.5:1), the results obtained using each corner detector are combined by calculating the average of corner coordinates using each model's confidence score as weights to produce the final output. The method for calculating the final corner positions using the confidence provided by each model as weights is expressed in Equation (7). \[C_{final}=\frac{\sum_{i-1}^{n}w_{i}\cdot C_{i}}{\sum_{i-1}^{n}w_{i}} \tag{7}\]  
Here, \(C_{final}\) is the final edge position, \(C_{i}\) is the edge position predicted by the \(i\)-th model, \(w_{i}\) is the confidence score (weight) of the corresponding model, and n is the number of models. Subsequently, the model's confidence is applied to each of the four corners (top-left, top-right, bottom-left, bottom-right) of the license plate.