A New U-Net Based License Plate Enhancement Model in Night and Day Images

A new trend of smart city development opens up many challenges. One such issue is that automatic vehicle driving and detection for toll fee payment in night or limited light environments. This paper presents a new work for enhancing license plates captured in limited or low light conditions such that license plate detection methods can be expanded to detect images at night. Due to the popularity of Convolutional Neural Network (CNN) in solving complex issues, we explore U-Net-CNN for enhancing contrast of license plate pixels. Since the difference between pixels that represent license plates and pixels that represent background is too due to low light effect, the special property of U-Net that extracts context and symmetric of license plate pixels to separate them from background pixels irrespective of content. This process results in image enhancement. To validate the enhancement results, we use text detection methods and based on text detection results we validate the proposed system. Experimental results on our newly constructed dataset which includes images captured in night/low light/limited light conditions and the benchmark dataset, namely, UCSD, which includes very poor quality and high quality images captured in day, show that the proposed method outperforms the existing methods. In addition, the results on text detection by different methods show that the proposed enhancement is effective and robust for license plate detection

A hierarchical RCNN for vehicle and vehicle license plate detection and recognition

Vehicle and vehicle license detection obtained incredible achievements during recent years that are also popularly used in real traffic scenarios, such as intelligent traffic monitoring systems, auto parking systems, and vehicle services. Computer vision attracted much attention in vehicle and vehicle license detection, benefit from image processing and machine learning technologies. However, the existing methods still have some issues with vehicle and vehicle license plate recognition, especially in a complex environment. In this paper, we propose a multivehicle detection and license plate recognition system based on a hierarchical region convolutional neural network (RCNN). Firstly, a higher level of RCNN is employed to extract vehicles from the original images or video frames. Secondly, the regions of the detected vehicles are input to a lower level (smaller) RCNN to detect the license plate. Thirdly, the detected license plate is split into single numbers. Finally, the individual numbers are recognized by an even smaller RCNN. The experiments on the real traffic database validated the proposed method. Compared with the commonly used all-in-one deep learning structure, the proposed hierarchical method deals with the license plate recognition task in multiple levels for sub-tasks, which enables the modification of network size and structure according to the complexity of sub-tasks. Therefore, the computation load is reduced

Modelo para la identificación de matrículas en la Ciudad de México mediante algoritmos de aprendizaje automático

Computer vision is one of the fields of Artificial Intelligence that is flourishing because it focuses on the development and improvement of techniques that allow computers to identify, process and classify images, in a way that resembles human vision. This feature makes them an excellent tool for vehicle control systems. For this reason, we developed a system for the recognition of Mexico City license plates using artificial vision techniques, image processing and automatic learning, in order to monitor and speed up response times, when a stolen vehicle is found.La visión artificial es uno de los campos de la Inteligencia Artificial que está en auge debido a que se centra en el desarrollo y mejoramiento de técnicas que permiten a las computadoras identificar, procesar y clasificar las imágenes de una manera similar a lo que hace la visión humana. Esta característica los vuelve una excelente herramienta para los sistemas de control vehicular. Por ello, nosotros desarrollamos un sistema para el reconocimiento de matrículas de la Ciudad de México mediante técnicas de visión artificial, procesamiento de imágenes y aprendizaje automático, con la finalidad de monitorear y agilizar los tiempos de respuesta en caso de encontrar un vehículo robado

Modelo para a identificação de placas na Cidade do México usando algoritmos de aprendizado de máquina

Computer vision is one of the fields of Artificial Intelligence that is flourishing because it focuses on the development and improvement of techniques that allow computers to identify, process and classify images, in a way that resembles human vision. This feature makes them an excellent tool for vehicle control systems. For this reason, we developed a system for the recognition of Mexico City license plates using artificial vision techniques, image processing and automatic learning, in order to monitor and speed up response times, when a stolen vehicle is found.La visión artificial es uno de los campos de la Inteligencia Artificial que está en auge debido a que se centra en el desarrollo y mejoramiento de técnicas que permiten a las computadoras identificar, procesar y clasificar las imágenes de una manera similar a lo que hace la visión humana. Esta característica los vuelve una excelente herramienta para los sistemas de control vehicular. Por ello, nosotros desarrollamos un sistema para el reconocimiento de matrículas de la Ciudad de México mediante técnicas de visión artificial, procesamiento de imágenes y aprendizaje automático, con la finalidad de monitorear y agilizar los tiempos de respuesta en caso de encontrar un vehículo robado.A visão artificial é um dos campos da Inteligência Artificial que está no auge debido a que se centralize no desarrollo e aprimoramento de técnicas que permite que o computador identifique, processe e classifique as imagens de uma maneira similar a lo que hace a visão humanos. Esta característica dos vuelve é uma excelente ferramenta para os sistemas de controle veicular. Por isso, nosotros desarrollamos um sistema para o reconhecimento de matrículas da Ciudad de México com técnicas de visão artificial, processamento de imagens e aprendizado automático, com a finalidad de monitorear e agilizar os tempos de resposta no caso de encontrar um veículo roubado

RECONOCIMIENTO DE CARACTERES ALFANUMÉRICOS HACIENDO USO DE MEMORIAS ASOCIATIVAS ALFA-BETA

ResumenEn la literatura se han utilizado muchos algoritmos de Inteligencia Artificial para el reconocimiento de textos en imágenes, algunos de estos métodos más utilizados son redes neuronales, las máquinas de vectores de soporte y el más común el reconocimiento de caracteres óptico por sus siglas en inglés (OCR). En este trabajo se presenta la utilización de un algoritmo mexicano para el reconocimiento de caracteres alfanuméricos llamado Memorias Asociativas Alfa-Beta programadas en el lenguaje de programación C#. Al entrenar el algoritmo con el método de validación K-Fold Cross Validation se obtuvo un índice de asertividad del 93% utilizando una base de datos de 10 patrones o imágenes por cada clase de números y letras con resolución de 100 x 200 pixeles. El método propuesto muestra una alta competitividad contra otros sistemas de reconocimiento de caracteres.Palabra(s) Clave: Caracteres, Reconocimiento, Memorias Asociativas. RECOGNITION OF ALPHANUMERIC CHARACTERS USING ASSOCIATIVE ALPHA-BETA MEMORIESAbstractIn the literature, many models of Artificial Intelligent (AI) have been used to text recognition in images. Some models more use are Artificial Neuronal Networks (ANN), support vector machine (SVM) and the most common Optical Character Recognition (OCR). This Work shows the use of a Mexican algorithm to alphanumeric character recognition called Memorias Asociativas Alfa-Beta, programming them in language C#. The algorithm was trained with K-Fold Cross Validation getting a 93% success rate. Our data base has 10 patters per every number and letter class with a resolution 100 x 200 pixels. The proposed method shows a high competitiveness against other character recognition systems.Keywords: Character, Memorias Asociativas, Recognitio

Full depth CNN classifier for handwritten and license plate characters recognition

Character recognition is an important research field of interest for many applications. In recent years, deep learning has made breakthroughs in image classification, especially for character recognition. However, convolutional neural networks (CNN) still deliver state-of-the-art results in this area. Motivated by the success of CNNs, this paper proposes a simple novel full depth stacked CNN architecture for Latin and Arabic handwritten alphanumeric characters that is also utilized for license plate (LP) characters recognition. The proposed architecture is constructed by four convolutional layers, two max-pooling layers, and one fully connected layer. This architecture is low-complex, fast, reliable and achieves very promising classification accuracy that may move the field forward in terms of low complexity, high accuracy and full feature extraction. The proposed approach is tested on four benchmarks for handwritten character datasets, Fashion-MNIST dataset, public LP character datasets and a newly introduced real LP isolated character dataset. The proposed approach tests report an error of only 0.28% for MNIST, 0.34% for MAHDB, 1.45% for AHCD, 3.81% for AIA9K, 5.00% for Fashion-MNIST, 0.26% for Saudi license plate character and 0.97% for Latin license plate characters datasets. The license plate characters include license plates from Turkey (TR), Europe (EU), USA, United Arab Emirates (UAE) and Kingdom of Saudi Arabia (KSA)

A new augmentation-based method for text detection in night and day license plate images

Despite a number of methods that have been developed for License Plate Detection (LPD), most of these focus on day images for license plate detection. As a result, license plate detection in night images is still an elusive goal for researchers. This paper presents a new method for LPD based on augmentation and Gradient Vector Flow (GVF) in night and day images. The augmentation involves expanding windows for each pixel in R, G and B color spaces of the input image until the process finds dominant pixels in both night and day license plate images of the respective color spaces. We propose to fuse the dominant pixels in R, G and B color spaces to restore missing pixels. For the results of fusing night and day images, the proposed method explores Gradient Vector Flow (GVF) patterns to eliminate false dominant pixels, which results in candidate pixels. The proposed method explores further GVF arrow patterns to define a unique loop pattern that represents hole in the characters, which gives candidate components. Furthermore, the proposed approach uses a recognition concept to fix the bounding boxes, merging the bounding boxes and eliminating false positives, resulting in text/license plate detection in both night and day images. Experimental results on night images of our dataset and day images of standard license plate datasets, demonstrate that the proposed approach is robust compared to the state-of-the-art methods. To show the effectiveness of the proposed method, we also tested our approach on standard natural scene datasets, namely, ICDAR 2015, MSRA-TD-500, ICDAR 2017-MLT, Total-Text, CTW1500 and MS-COCO datasets, and their results are discussed

An Adaptive Approach for Multi-National Vehicle License Plate Recognition Using Multi-Level Deep Features and Foreground Polarity Detection Model

License plate recognition system (LPR) plays a vital role in intelligent transport systems to build up smart environments. Numerous country specific methods have been proposed successfully for an LPR system, but there is a need to find a generalized solution that is independent of license plate layout. The proposed architecture is comprised of two important LPR stages: (i) License plate character segmentation (LPCS) and (ii) License plate character recognition (LPCR). A foreground polarity detection model is proposed by using a Red-Green-Blue (RGB) channel-based color map in order to segment and recognize the LP characters effectively at both LPCS and LPCR stages respectively. Further, a multi-channel CNN framework with layer aggregation module is proposed to extract deep features, and support vector machine is used to produce target labels. Multi-channel processing with merged features from different-level convolutional layers makes output feature map more expressive. Experimental results show that the proposed method is capable of achieving high recognition rate for multinational vehicles license plates under various illumination conditions. Document type: Articl

Prosiding Seminar Nasional Teknologi Terapan Berbasis Kearifan Lokal

Ruang terbuka hijau kota memiliki fungsi utama sebagai penunjang ekologis kota yang juga diperuntukkan sebagai ruang terbuka penambah dan pendukung nilai kualitas lingkungan dan budaya suatu kawasan. Kota Unaaha harus memperhitungkan perkembangan kota dimasa yang akan datang dengan suatu perencanaan, penyediaan dan pengelolaan RTH di perkotaan yang diharapkan nantinya dapat terwujud ruang kota yang nyaman, produktif dan berkelanjutan, serta terwujudnya kota hijau yang ramah lingkungan. Tujuan penelitian ini adalah melihat kecenderungan pengelolaan RTH Perkotaan Unaaha dan pengembangan RTH kedepannya. Metode pendekatan yang digunakan yaitu metode deskripsi kuantitatif dan kualitatif, serta metode analisis keruangan (spasial). Hasil penelitian menunjukkan bahwa penyediaan ruang terbuka hijau perkotaan berdasarkan luas wilayah yaitu 4542,6 ha (pemanfaatan RTH perkotaan Unaaha hanya sekitar 6%), dan penyedian RTH berdasarkan jumlah penduduk sebesar 107,65 ha (pemanfaatan RTH perkotaan Unaaha hanya sekitar 2,37%)