Licence Plate Detection Using Machine Learning

License Plate Recognition (LPR) is one of the tough tasks in the field of computer vision. Although it has been around for quite a while, there still lies the challenges when we have to deal with; the harsh environmental conditions like snowy, rainfall, windy, low light conditions etc. as well as the condition of the plates which includes the bent, rotated, broken plates. The performance of the recognition and detection frameworks take a significant hit when it is concerned with these conditional effects on the license plate. In this paper, we introduced a model to improve our accuracy based on the Chinese Car Parking Dataset (CCPD) using 2 separate convolutional neural networks. The first CNN will be able to detect the bounding boxes for the license plate detection using Non-Maximus Suppression (NMS) to find the most probable bounding area whereas the second one will take these bounding boxes and use the spatial attenuation network and character recognition model to successfully recognize the license plate. First, we train the CNN to detect the license plates, then use the second CNN to recognize the characters. The overall recognition accuracy was found to be 89% in the CCPD dataset

IEEE Access special section editorial: scalable deep learning for big data

Deep learning (DL) has emerged as a key application exploiting the increasing computational power in systems such as GPUs, multicore processors, Systems-on-Chip (SoC), and distributed clusters. It has also attracted much attention in discovering correlation patterns in data in an unsupervised manner and has been applied in various domains including speech recognition, image classification, natural language processing, and computer vision. Unlike traditional machine learning (ML) approaches, DL also enables dynamic discovery of features from data. In addition, now, a number of commercial vendors also offer accelerators for deep learning systems (such as Nvidia, Intel, and Huawei)

IoT solution for tourism promotion in Smart Cities using Computer Vision techniques in Cloud environments

Máster en Internet de las Cosas, Facultad de Informática UCM, Departamento de Ingeniería de Software e Inteligencia Artificial, Curso 2020/2021.En el contexto del Internet de las Cosas (IoT, Internet of Things), y en particular de las Ciudades Inteligentes (Smart Cities), se ha diseñado y desplegado en distintos entornos Cloud una solución que permite a cualquier comercio/institución publicar, a través de distintos canales, información que pudiera resultar de interés a fin de promocionar los servicios que ofrece independientemente de la actividad que desarrolle. Toda esta información podrá ser consultada por los viandantes que se encuentren frente a ellos mediante una fotografía donde se señale con el dedo índice al panel informativo que los identifica. Se ha desarrollado un modelo de detección de objetos capaz de identificar a partir de estas imágenes, en cooperación con servicios de reconocimiento de caracteres, la fuente de información solicitada a fin de recuperarla. El módulo de detección se ha diseñado utilizando tecnologías de Aprendizaje Profundo, concretamente el modelo YOLOv3, como método eficiente. Con el fin de explotar las capacidades de este modelo, y de recuperar la información publicada de antemano, se ha provisionado en entornos sin servidor un servicio diseñado para atender a las distintas solicitudes de los usuarios de la solución que este trabajo propone. La solución conceptual desarrollada permite verificar su validez en el contexto propuesto y bajo el paradigma IoT, quedando así disponible para su adaptación y despliegue en entornos reales.In the context of the Internet of Things (IoT), and in particular of the Smart Cities, a solution has been designed and deployed in different Cloud environments that allows any company/institution to publish, through different channels, information that may be of interest to promote the services it offers, regardless of the activity it develops. All this information will be able to be consulted by the pedestrians in front of them by a photograph where they point with their index finger at the information panel that identifies them. An object detection model has been developed capable of identifying from these images, in collaboration with the character recognition services, the source of information requested to recover it. The detection module has been designed using Deep Learning technologies, specifically the YOLOv3 model, as an efficient method. In order to exploit the capabilities of this model, and to recover the nformation published in advance, a service designed to meet the requests of users of the solution proposed in this work has been provided through serverless environments. The conceptual solution developed allows to verify its validity in the proposed context and under the IoT paradigm, thus remaining available for its adaptation and deployment in real environments.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

The Effects of the COVID-19 Pandemic on the Digital Competence of Educators

The Covid-19 pandemic is having an undeniable impact on all the statements of society. Regarding teaching and learning activities, most educational institutions suspended in-person instruction and moved to remote learning during the lockdown of March and April 2020. Although nowadays many countries have progressively re-opened their educational systems, blended learning is a common practice aimed to reduce the spread of the Covid-19 disease. This disruption has supposed an unprecedented acceleration to the digitalization of teaching and learning. Teaching professionals have been forced to develop their digital competence in a short amount of time, getting mastery in the management of information, the creation of audiovisual contents, and the use of technology to keep their students connected. This Special Issue presents contributions regarding the adoption of distance learning strategies, experiences, or lessons learned in this domain