## 2 Related Work
In [9], the researchers propose ALPRNet, a single neural network for the detection and recognition of mixed-style license plates. ALPRNet uses two fully convolutional one-stage object detectors to simultaneously detect and classify license plates and characters, followed by an assembly module to output the license plate strings. Experimental results show that ALPRNet achieves state-of-the-art results with a simple one-stage network. One study [10] focuses on the detection and recognition of Chinese license plates in complex situations using datasets from the Chinese City Parking Dataset. The authors propose a two-stage license plate recognition algorithm based on YOLOv3 and the Improved License Plate Recognition Net (ILPRNET). The test results indicate that the proposed algorithm works well in diverse complex scenarios, with recognition accuracy reaching up to 99.2% on some datasets. The presented research shares the common use of convolutional neural networks and well-known datasets such as \"Caltech1999-2001\" [11], \"PKU-G1, G2, G3, G4, and G5\", the \"Synthetic dataset\", the \"Stanford cars dataset\" [12], \"CCPD\", \"UFPR-ALPR\", and \"SaudiLP\" [13]. However, it is also common to find research using personal, non-public databases. Given the works presented, the detection of vehicle license plates and the identification of their characters remains an ongoing problem with multiple approaches. Due to the high performance of CNNs, they have become the preferred technique for developing models, whether with known or proprietary datasets. The following section presents research focused on the use of bio-inspired models for scene improvement or feature extraction to obtain better results in different applications. It is important to note that computer vision is still an area of current research and is constantly evolving [14; 15; 16; 17; 18; 19]. Different techniques require reinforcement of system responses to improve results, such as convolutional neural networks [20; 21; 22] and the use of a bio-inspired retina that allows the creation of filters for the visual preprocessing stage, analogous to the functioning of some biological retinas. This type of filter is applicable to multiple applications such as navigational robots [23], pattern recognition, and improvements in tone mapping, among others. In [24], the authors propose a bio-inspired system based on the retina of fish to solve the problems of underwater image degradation caused by color distortion and non-uniform light polarization. The adaptive nature of this bio-inspired model allows it to operate without knowing the previous conditions of the water. The authors conclude that their algorithm fuses complementary information on luminosity provided by the ON-OFF response of the designed retina, which differs from other fusion methods. From an implementation perspective, the application of this technique in reconnaissance aquatic robots is fascinating. The research described in [25] found that bio-inspired retinal models allow reduced processing time and applied these techniques to unmanned or autonomous vehicles. The results support the idea that the decision-making capabilities of the robots became faster and more accurate. Furthermore, the authors argue that bio-inspired retinal models supported by a reinforcement learning method prove very reliable for decision-making. A proposed bio-inspired convolutional neural network layer, named Q9, is presented in [26]. This layer is designed to provide an invariant response to high-contrast changes and maintain consistent performance in classification tasks, even when the convolutional neural network is trained with different levels of contrast. Additionally, the authors claim that Q9 enables faster learning of image rotation angles than a standard convolutional layer. This demonstrates that bio-inspired retinal models offer improved performance in computational tasks when training a CNN.