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

Development of software for the segmentation of text areas in real-scene images

Работа посвящена проектированию и реализации нейросетевого алгоритма детектирования надписей на изображениях реальных сцен. Проведен обзор существующих нейросетевых и классических моделей, в качестве базовой была выбрана модель U-net. На ее основе предложен и реализован алгоритм детектирования текстовых областей на изображениях. В ходе проведения экспериментов были определены следующие параметры нейронной сети: размеры входных изображений, количество и типы составляющих её слоёв. В качестве предобработки рассматривались билатеральные фильтры сглаживания и сглаживающие частотные фильтры. Увеличение исходной базы изображений KAIST Scene Text Database достигается за счёт применения поворотов, сжатия и разбиения входящих в неё изображений. Полученные результаты превосходят другие методы по значению F-меры и достигают 0,88

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

Разработка нейросетевого алгоритма распознавания надписей на изображениях реальных сцен

Объектом проектирования и разработки является нейросетевой алгоритм распознавания надписей на изображениях реальных сцен. В процессе исследования был проведён анализ существующих моделей обнаружения текстовых областей на изображениях реальных сцен, на основании которого был произведён выбор модели для дальнейшей реализации. В результате был предложен нейросетевой алгоритм распознавания надписей на изображениях реальных сцен. В ходе проведения тестирования были подобраны такие параметры нейронной сети как размеры входных изображений, количество и типы используемых слоёв. В результате конечного обучения нейросетевого алгоритма удалось добиться высокой точности.The object of design and development is a neural network algorithm for text recognition on images of real scenes. In the process of researching analytical reviews of existing methods for detecting text areas on images of real scenes were conducted. A model for implementation was chosen. As a result, a neural network algorithm for text recognition on images of real scenes was proposed. As a stage for the selection of the neural network parameters, such parameters as the size of the input images, the number and types of layers used were changed. High accuracy was achieved during the training part