Fastest Moroccan license plate recognition using a lightweight modified YOLOv5 model

Abstract

Morocco is witnessing an alarming surge in road accidents. Automatic license plate recognition (ALPR) technology is vital in enhancing road safety. It enables applications like traffic management, law enforcement, and toll collection by automatically identifying vehicles on the roads. This paper integrated the ShuffleNet V2 architecture into the end-to-end YOLOv5 object detection system. The goal was to develop a model capable of accurately detecting Moroccan license plates with an 87% accuracy rate. The proposed model was able to achieve high processing speeds of 60 frames per second (FPS) while maintaining a compact size of 1.3 megabytes and a limited computational requirement of 0.44 million floating-point operations. Compared to other models used in similar contexts, this model demonstrates superior performance and high compatibility with embedded systems, making it a promising solution for addressing road safety challenges in Morocco.