Detecting Vehicles Number Plates using CNN and Particle Swarm Optimization

The application of intelligent traffic management systems relies chiefly on the detection and recognition of the license plate of vehicles. Convolutional Neural Networks (CNNs) have typical use when such identification is performed because that can recognize complex patterns in images. However, the existing methodological system such as OKM-CNN has proven viability-related challenges, which include license plates with different languages, motion blur, and different lighting conditions familiar with real circumstances. In addition, the models have been designed with fixed sets, which limit their flexibility and adaptability in various situations. The core objective of this research work is to solve the above addressed problems by proposing a system that integrates CNN classifier with Particle Swarm Optimization (PSO) technique. The recognition power of the patterns creates the potential to recognize features involved in license number plates and their characters. PSO is such an approach, inspired by bird movement in a flock, to tune the settings of CNN automatically. This merger enables the system to make more accurate identification of license plates during challenging situations while cars are running. The outcome is a highly precise, flexible, and contextually suitable model ready for modern traffic management systems.