Enhancing Public Safety through License Plate Recognition for Counter terrorism through Deep Learning Technique

License plate recognition (LPR) is a key technology for enhancing public safety and aiding counterterrorism efforts. In this study, we aimed to develop an LPR system based on YOLOv5 that can accurately recognise licence plates from surveillance cameras and other sources in real-time. The aim of this study was to evaluate the performance of a YOLOv5-based LPR system on a dataset of labeled license plate images and assess its potential for enhancing public safety and aiding counterterrorism efforts. We used a publicly available dataset of 10,000 labeled license plate images to train and test our YOLOv5-based LPR system. The dataset included various license plate types, angles, and lighting conditions to ensure the robustness and generalization of the model. Results: Our YOLOv5-based LPR system achieved high accuracy on the test dataset (93%). The system was able to detect and recognise licence plates in realtime with an average speed of 30 frames per second, making it suitable for deployment in real-world scenarios. In conclusion, our study demonstrates the potential of YOLOv5 for licence plate recognition to enhance public safety and aid counterterrorism efforts. The high accuracy, speed, and real-time performance of our system make it a promising tool for surveillance systems, law enforcement agencies, and other organisations that prioritise public safety and security. Future research can further optimise and improve the performance of YOLOv5-based LPR systems by incorporating other deep learning techniques and using larger and more diverse datasets.