An Efficient and Robust Deep Learning Approach for Vehicle Recognition using Light-weight Deep Network

In the realm of intelligent transportation systems, automatic number plate detectionhas emerged as a crucial research topic due to its wide range of applications, including traffic violation monitoring, support for autonomous vehicles, vehicle speed tracking, automated toll collection, stolen vehicle identification, and overall traffic management. The goal of automatic number plate detection is to accurately identify vehicles based on their number plates. This study proposes a hierarchicalapproach for detecting number plates. In the initial phase, a lightweight deep learning model, Mobile Net-SSD, is employed to detect number plates. Subsequently, the alphanumeric characters from the detected number plates are extracted using an Optical Character Recognition (OCR) technique. The model is, built on a convolutional neural network, and efficiently uses depth wiseand pointwise convolution layers, making it suitable for mobile and embedded systems. Additionally, we introduce a dataset of 30,613 vehicle number plate images to foster further research. Experimental evaluations show that the proposed method achieves 95% accuracy on this dataset, significantly enhancing real-time number plate detection and making it suitable for large-scale implementations in smart cities and intelligent transportation networks.