5 research outputs found
Deep Learning Based Automatic Vehicle License Plate Recognition System for Enhanced Vehicle Identification
An innovative Automatic Vehicle License Plate Recognition (AVLPR) system that effectively identifies vehicles using deep learning algorithms. Accurate and real-time license plate identification has grown in importance with the rise in demand for improved security and traffic management.The convolutional neural network (CNN) architecture used in the AVLPR system enables the model to automatically learn and extract discriminative characteristics from photos of license plates. To ensure the system's robustness and adaptability, the dataset utilized for training and validation includes a wide range of license plate designs, fonts, and lighting situations.We incorporate data augmentation approaches to accommodate differences in license plate orientation, scale, and perspective throughout the training process to improve recognition accuracy. Additionally, we use transfer learning to enhance the system's generalization abilities by refining the pre-trained model on a sizable dataset.A trustworthy and effective solution for vehicle identification duties is provided by the Deep Learning-Based Automatic Vehicle License Plate Recognition System. Deep learning approaches are used to guarantee precise and instantaneous recognition, making it suitable for many uses such as law enforcement, parking management, and intelligent transportation systems
Holistically-Attracted Wireframe Parsing
This paper presents a fast and parsimonious parsing method to accurately and
robustly detect a vectorized wireframe in an input image with a single forward
pass. The proposed method is end-to-end trainable, consisting of three
components: (i) line segment and junction proposal generation, (ii) line
segment and junction matching, and (iii) line segment and junction
verification. For computing line segment proposals, a novel exact dual
representation is proposed which exploits a parsimonious geometric
reparameterization for line segments and forms a holistic 4-dimensional
attraction field map for an input image. Junctions can be treated as the
"basins" in the attraction field. The proposed method is thus called
Holistically-Attracted Wireframe Parser (HAWP). In experiments, the proposed
method is tested on two benchmarks, the Wireframe dataset, and the YorkUrban
dataset. On both benchmarks, it obtains state-of-the-art performance in terms
of accuracy and efficiency. For example, on the Wireframe dataset, compared to
the previous state-of-the-art method L-CNN, it improves the challenging mean
structural average precision (msAP) by a large margin ( absolute
improvements) and achieves 29.5 FPS on single GPU ( relative
improvement). A systematic ablation study is performed to further justify the
proposed method.Comment: Accepted by CVPR 202
Analysis of camera pose estimation using 2D scene features for augmented reality applications
La réalité augmentée (RA) a récemment eu un impact énorme sur les ingénieurs civils et les travailleurs de l'industrie de la construction, ainsi que sur leur interaction avec les plans ar-chitecturaux. La RA introduit une superposition du modèle 3D d'un bâtiment sur une image 2D non seulement comme une image globale, mais aussi potentiellement comme une repré-sentation complexe de ce qui va être construit et qui peut être visualisée par l'utilisateur. Pour insérer un modèle 3D, la caméra doit être localisée par rapport à son environnement. La lo-calisation de la caméra consiste à trouver les paramètres extérieurs de la caméra (i.e. sa po-sition et son orientation) par rapport à la scène observée et ses caractéristiques. Dans ce mémoire, des méthodes d'estimation de la pose de la caméra (position et orientation) par rapport à la scène utilisant des correspondances cercle-ellipse et lignes droites-lignes droites sont explorées. Les cercles et les lignes sont deux des caractéristiques géométriques qui sont principalement présentes dans les structures et les bâtiments. En fonction de la rela-tion entre les caractéristiques 3D et leurs images 2D correspondantes détectées dans l'image, la position et l'orientation de la caméra sont estimées.Augmented reality (AR) had recently made a huge impact on field engineers and workers in construction industry, as well as the way they interact with architectural plans. AR brings in a superimposition of the 3D model of a building onto the 2D image not only as the big picture, but also as an intricate representation of what is going to be built. In order to insert a 3D model, the camera has to be localized regarding its surroundings. Camera localization con-sists of finding the exterior parameters (i.e. its position and orientation) of the camera with respect to the viewed scene and its characteristics. In this thesis, camera pose estimation methods using circle-ellipse and straight line corre-spondences has been investigated. Circles and lines are two of the geometrical features that are mostly present in structures and buildings. Based on the relationship between the 3D features and their corresponding 2D data detected in the image, the position and orientation of the camera is estimated