## 4 Datasets  
In the realm of model training, datasets assume an indispensable role. Models heavily depend on the intricate patterns and distinct characteristics found within datasets to draw inferences and make predictions. The absence of access to a dataset hampers a model's ability to grasp these features, thereby rendering it incapable of effective training or practical application [21]. Training a model with meticulously curated, high-quality, and properly segmented datasets yields experimental results that are not only objective but also highly persuasive. In the domain of license plate recognition technology, recognized datasets such as CCPD [22] and CRPD [23] are prevalent. While these datasets provide ample data for model training, they are plagued by certain limitations. Typically, these datasets encompass a limited variety of license plate types, possess restricted geographical coverage, feature license plate images of subpar quality, and lack appropriate proportioning for training, validation, and testing sets [24]. Consequently, models trained on such datasets tend to yield results that are susceptible to subjective influences. This leads to reduced persuasiveness and limited practical applicability. Furthermore, these models fail to adequately mirror the complexities posed by various intricate conditions encountered in real-world scenarios [25].  
In order to ensure the objectivity of the model in real-world applications after the training phase, we meticulously curated a specialized dataset called the Complex Parking Dataset (CMPD), tailored specifically for real-world traffic scenarios in China. This dataset was meticulously constructed by amalgamating publicly accessible datasets, including the CCPD (Chinese City Parking Dataset), provided by researchers at the University of Science and Technology of China; the CRPD (Chinese Road Plate Dataset), offered by scholars at the University of Electronic Science and Technology of China; and the CBLPRD (China-Balanced- License-Plate-Recognition-Dataset) from various academic sources. Additionally, we integrated self-captured data from diverse locations within Xiamen City. Following data  
Figure 2: Channel transformation module.  
cleaning and labeling, we have crafted a dataset tailored to real-world traffic applications, ensuring its relevance and applicability in practical scenarios, as visually depicted in Figure 3. The dataset was partitioned into training (comprising 7000 images), validation (consisting of 2000 images), and test sets (comprising 1000 images) in a ratio of 7:2:1. This meticulous partitioning method was implemented to provide robust data support for model testing, continuous learning, and precise evaluation of model performance. Our dataset encompasses a comprehensive collection of license plate images originating from all provinces in China. This dataset includes a diverse range of special vehicle license plates, such as those belonging to traditional fuel vehicles, new-energy vehicles, foreign consulates, police cars, instructor vehicles, civil aviation vehicles, as well as plates from Guangdong and Hong Kong, and specialized operation vehicles. To enhance the robustness of our trained models, we have incorporated hybrid license plates composed of both traditional fuel and new-energy vehicle plates. To augment the complexity of the dataset, we introduced noise and low-quality images with various interferences. These additions simulate real-world traffic scenarios where license plates undergo deformation, face adverse weather conditions, and encounter high speeds and uneven lighting, among other challenging recognition situations. By integrating such diverse and challenging instances, our dataset not only broadens the spectrum of license plate types but also elevates the model's ability to generalize. This enriched dataset significantly contributes to the current landscape of license plate recognition datasets, substantially increasing the sample size and enhancing the model's overall performance and adaptability.