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 en- ables 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 sys- tem. The goal was to develop a model capable of accurately detecting Moroc- can license plates with an 87% accuracy rate. The proposed model was able to achieve high processing speeds of 60 frames per second (FPS) while maintain- ing 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 com- patibility with embedded systems, making it a promising solution for addressing road safety challenges in Morocco.