Recognition and Detection of Vehicle License Plates Using Convolutional Neural Networks

The rise in toll road usage has sparked a lot of interest in the newest, most effective, and most innovative intelligent transportation system (ITS), such as the Vehicle License Plate Recognition (VLPR) approach. This research uses Convolutional Neural Networks to deliver effective deep learning principally based on Automatic License Plate Recognition (ALPR) for detection and recognition of numerous License Plates (LPs) (CNN). Two fully convolutional one-stage object detectors are utilized in ALPRNet to concurrently identify and categorize LPs and characters, followed by an assembly module that outputs the LP strings. Object detectors are typically employed in CNN-based approaches such as You Only Look Once (YOLO), Faster Region-based Convolutional Neural Network (Faster R-CNN), and Mask Region-based Convolutional Neural Network (Mask R-CNN) to locate LPs. The VLPR model is used here to detect license plates using You Only Look Once (YOLO) and to recognize characters in license plates using Optical Character Recognition (OCR). Unlike existing methods, which treat license plate detection and recognition as two independent problems to be solved one at a time, the proposed method accomplishes both goals using a single network. Matlab R2020a was used as a tool

Recognition and Detection of Vehicle License Plates Using Convolutional Neural Networks

The rise in toll road usage has sparked a lot of interest in the newest, most effective, and most innovative intelligent transportation system (ITS), such as the Vehicle License Plate Recognition (VLPR) approach. This research uses Convolutional Neural Networks to deliver effective deep learning principally based on Automatic License Plate Recognition (ALPR) for detection and recognition of numerous License Plates (LPs) (CNN). Two fully convolutional one-stage object detectors are utilized in ALPRNet to concurrently identify and categorize LPs and characters, followed by an assembly module that outputs the LP strings. Object detectors are typically employed in CNN-based approaches such as You Only Look Once (YOLO), Faster Region-based Convolutional Neural Network (Faster R-CNN), and Mask Region-based Convolutional Neural Network (Mask R-CNN) to locate LPs. The VLPR model is used here to detect license plates using You Only Look Once (YOLO) and to recognize characters in license plates using Optical Character Recognition (OCR). Unlike existing methods, which treat license plate detection and recognition as two independent problems to be solved one at a time, the proposed method accomplishes both goals using a single network. Matlab R2020a was used as a tool

DEEP LEARNING FRAMEWORK FOR CHARACTER RECOGNITION IN LOW QUALITY LICENSE PLATE IMAGES

Commercially available Automatic License Plate Recognition (ALPR) systems have limited ability to provide character recognition on low-quality license plate images [20]. Improving this ability would be beneficial for tasks currently requiring human involvement to read low-quality license plate characters. Recent advances in Deep Learning networks have shown that, for object detection tasks, Deep Learning networks can achieve levels of performance equal to or better than those of a human [2,6]. The aim of this thesis is to introduce a foundational Deep Learning framework for character recognition performance analysis. The analysis is carried out on license plate images that have undergone various types and levels of image quality reduction. This thesis leverages the TensorFlow Object Detection API to enable rapid development and testing of different Machine Learning networks and configurations. The framework allows for the creation of synthetically generated datasets on which image augmentation techniques can be applied. The various image augmentation techniques expand the dataset, and enable the network to be robust to image quality reductions. Networks were trained on the Maryland Advanced Computing Center’s GPU system. Per-character metrics of precision and recall are framework outputs used to evaluate trained networks. Network performance was evaluated using the framework for several Machine Learning models. The Faster R-CNN ResNet 50 network was found to have the best performance for character recognition on synthetically generated license plate images. On an ideal dataset, with no image degradation applied, the lower threshold of image size, on which the Faster R-CNN ResNet 50 network can reliably perform character recognition, was found to be 32 x 16 pixels. Finally, the network was trained and tested on image datasets with various data augmentation techniques applied. The data augmentation techniques evaluated in this thesis are: JPEG Compression, motion blur, affine transforms, and Gaussian noise. The results showed that, when trained on augmented synthetic data, the network was robust to quality reduction from most of the applied augmentation techniques

SHINE: Deep Learning-Based Accessible Parking Management System

The ongoing expansion of urban areas facilitated by advancements in science and technology has resulted in a considerable increase in the number of privately owned vehicles worldwide, including in South Korea. However, this gradual increment in the number of vehicles has inevitably led to parking-related issues, including the abuse of disabled parking spaces (hereafter referred to as accessible parking spaces) designated for individuals with disabilities. Traditional license plate recognition (LPR) systems have proven inefficient in addressing such a problem in real-time due to the high frame rate of surveillance cameras, the presence of natural and artificial noise, and variations in lighting and weather conditions that impede detection and recognition by these systems. With the growing concept of parking 4.0, many sensors, IoT and deep learning-based approaches have been applied to automatic LPR and parking management systems. Nonetheless, the studies show a need for a robust and efficient model for managing accessible parking spaces in South Korea. To address this, we have proposed a novel system called, Shine, which uses the deep learning-based object detection algorithm for detecting the vehicle, license plate, and disability badges (referred to as cards, badges, or access badges hereafter) and verifies the rights of the driver to use accessible parking spaces by coordinating with the central server. Our model, which achieves a mean average precision of 92.16%, is expected to address the issue of accessible parking space abuse and contributes significantly towards efficient and effective parking management in urban environments

A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

Automatic License Plate Recognition (ALPR) has been a frequent topic of research due to many practical applications. However, many of the current solutions are still not robust in real-world situations, commonly depending on many constraints. This paper presents a robust and efficient ALPR system based on the state-of-the-art YOLO object detector. The Convolutional Neural Networks (CNNs) are trained and fine-tuned for each ALPR stage so that they are robust under different conditions (e.g., variations in camera, lighting, and background). Specially for character segmentation and recognition, we design a two-stage approach employing simple data augmentation tricks such as inverted License Plates (LPs) and flipped characters. The resulting ALPR approach achieved impressive results in two datasets. First, in the SSIG dataset, composed of 2,000 frames from 101 vehicle videos, our system achieved a recognition rate of 93.53% and 47 Frames Per Second (FPS), performing better than both Sighthound and OpenALPR commercial systems (89.80% and 93.03%, respectively) and considerably outperforming previous results (81.80%). Second, targeting a more realistic scenario, we introduce a larger public dataset, called UFPR-ALPR dataset, designed to ALPR. This dataset contains 150 videos and 4,500 frames captured when both camera and vehicles are moving and also contains different types of vehicles (cars, motorcycles, buses and trucks). In our proposed dataset, the trial versions of commercial systems achieved recognition rates below 70%. On the other hand, our system performed better, with recognition rate of 78.33% and 35 FPS.Comment: Accepted for presentation at the International Joint Conference on Neural Networks (IJCNN) 201