Automatic Number Plate Recognition on FPGA

Automatic Number Plate Recognition (ANPR) systems have become one of the most important components in the current Intelligent Transportation Systems (ITS). In this paper, a FPGA implementation of a complete ANPR system which consists of Number Plate Localisation (NPL), Character Segmentation (CS), and Optical Character Recognition (OCR) is presented. The Mentor Graphics RC240 FPGA development board was used for the implementation, where only 80% of the available on-chip slices of a Virtex-4 LX60 FPGA have been used. The whole system runs with a maximum frequency of 57.6 MHz and is capable of processing one image in 11ms with a successful recognition rate of 93%

Real-Time Vision System for License Plate Detection and Recognition on FPGA

Rapid development of the Field Programmable Gate Array (FPGA) offers an alternative way to provide acceleration for computationally intensive tasks such as digital signal and image processing. Its ability to perform parallel processing shows the potential in implementing a high speed vision system. Out of numerous applications of computer vision, this paper focuses on the hardware implementation of one that is commercially known as Automatic Number Plate Recognition (ANPR).Morphological operations and Optical Character Recognition (OCR) algorithms have been implemented on a Xilinx Zynq-7000 All-Programmable SoC to realize the functions of an ANPR system. Test results have shown that the designed and implemented processing pipeline that consumed 63 % of the logic resources is capable of delivering the results with relatively low error rate. Most importantly, the computation time satisfies the real-time requirement for many ANPR applications

IMPROVED LICENSE PLATE LOCALIZATION ALGORITHM BASED ON MORPHOLOGICAL OPERATIONS

Automatic License Plate Recognition (ALPR) systems have become an important tool to track stolen cars, access control, and monitor traffic. ALPR system consists of locating the license plate in an image, followed by character detection and recognition. Since the license plate can exist anywhere within an image, localization is the most important part of ALPR and requires greater processing time. Most ALPR systems are computationally intensive and require a high-performance computer. The proposed algorithm differs significantly from those utilized in previous ALPR technologies by offering a fast algorithm, composed of structural elements which more precisely conducts morphological operations within an image, and can be implemented in portable devices with low computation capabilities. The proposed algorithm is able to accurately detect and differentiate license plates in complex images. This method was first tested through MATLAB with an on-line public database of Greek license plates which is a popular benchmark used in previous works. The proposed algorithm was 100% accurate in all clear images, and achieved 98.45% accuracy when using the entire database which included complex backgrounds and license plates obscured by shadow and dirt. Second, the efficiency of the algorithm was tested in devices with low computational processing power, by translating the code to Python, and was 300% faster than previous work

A Review of Automatic License Plate Recognition System in Mobile based Platform

Automatic license plate recognition (ALPR) is the process of retrieving license plate information from a captured image or video frames from a sequence of videos. ALPR can assist law enforcement officers to identify stolen vehicles or to capture vehicle information from those that violate traffic laws instantly. It is also commonly used as an electronic payment system for toll payment or parking fee payment. Traditionally, ALPR is installed in a PC-based platform to take advantage of its processing power to process high-quality images captured by high-resolution cameras. Most smartphones nowadays are equipped with a high-quality camera and faster processing system which can be used to develop portable ALPR system. Thus, this has encouraged many researchers to work on implementing ALPR technology for the mobile platform. In this paper, we reviewed several researches that have implemented ALPR in the mobile-based platform. We discuss the techniques used in the three main stages of ALPR namely localisation, segmentation and recognition. The advantages and disadvantages of each technique are summarised in a table

A design of license plate recognition system using convolutional neural network

This paper proposes an improved Convolutional Neural Network (CNN) algorithm approach for license plate recognition system. The main contribution of this work is on the methodology to determine the best model for four-layered CNN architecture that has been used as the recognition method. This is achieved by validating the best parameters of the enhanced Stochastic Diagonal Levenberg Marquardt (SDLM) learning algorithm and network size of CNN. Several preprocessing algorithms such as Sobel operator edge detection, morphological operation and connected component analysis have been used to localize the license plate, isolate and segment the characters respectively before feeding the input to CNN. It is found that the proposed model is superior when subjected to multi-scaling and variations of input patterns. As a result, the license plate preprocessing stage achieved 74.7% accuracy and CNN recognition stage achieved 94.6% accuracy

Analysis of the Variety of Lithium-Ion Battery Modules and the Challenges for an Agile Automated Disassembly System

Within this paper the initial steps for the realisation of an agile automated system for battery module disassembly will be presented. The state of the art battery modules need to be analysed with regards to their structure, components and the relationship of the components to each other. In particular, the key challenges in battery module disassembly up to cell level are identified and classified in order to systematically derive the requirements for the disassembly system. The identified challenges for automated disassembly are twofold: process-related and product-related. The variety of battery modules can be seen as a product-related challenge, while non-detachable joints combined with the hazards posed by Li-ion batteries can be described as process-related challenge. An approach for capturing the variety of battery modules is done by using the methodology of a morphological box

Combined flow cytometry and high-throughput image analysis for the study of essential genes in Caenorhabditis elegans

Background: Advances in automated image-based microscopy platforms coupled with high-throughput liquid workflows have facilitated the design of large-scale screens utilising multicellular model organisms such as Caenorhabditis elegans to identify genetic interactions, therapeutic drugs or disease modifiers. However, the analysis of essential genes has lagged behind because lethal or sterile mutations pose a bottleneck for high-throughput approaches, and a systematic way to analyse genetic interactions of essential genes in multicellular organisms has been lacking. Results: In C. elegans, non-conditional lethal mutations can be maintained in heterozygosity using chromosome balancers, commonly expressing green fluorescent protein (GFP) in the pharynx. However, gene expression or function is typically monitored by the use of fluorescent reporters marked with the same fluorophore, presenting a challenge to sort worm populations of interest, particularly at early larval stages. Here, we develop a sorting strategy capable of selecting homozygous mutants carrying a GFP stress reporter from GFP-balanced animals at the second larval stage. Because sorting is not completely error-free, we develop an automated high-throughput image analysis protocol that identifies and discards animals carrying the chromosome balancer. We demonstrate the experimental usefulness of combining sorting of homozygous lethal mutants and automated image analysis in a functional genomic RNA interference (RNAi) screen for genes that genetically interact with mitochondrial prohibitin (PHB). Lack of PHB results in embryonic lethality, while homozygous PHB deletion mutants develop into sterile adults due to maternal contribution and strongly induce the mitochondrial unfolded protein response (UPR mt ). In a chromosome-wide RNAi screen for C. elegans genes having human orthologues, we uncover both known and new PHB genetic interactors affecting the UPR mt and growth. Conclusions: The method presented here allows the study of balanced lethal mutations in a high-throughput manner. It can be easily adapted depending on the user's requirements and should serve as a useful resource for the C. elegans community for probing new biological aspects of essential nematode genes as well as the generation of more comprehensive genetic networks.European Research Council ERC-2011-StG-281691Ministerio de Economía y Competitividad BFU2012–3550

Using phased array ultrasound to localize probes during the inspection of welds

In this paper, an image processing-based localization system is developed for remote nondestructive evaluation of welds within industrial assets. Manual ultrasonic inspection of large-scale structures is often repetitive, time-consuming, and benefits greatly from robotic support, however, these robotic systems are often fixed to a single purpose, lack self-awareness of their surrounding environment, and can be limited to simple geometry. For the inspection of welds, which are often carried out using phased array ultrasonic testing, there is a reliance on the use of surface features for automated tracking such as the laser profiling of a weld cap. For the inspection of more complex geometry such as non-linear or saddle welds a more positionally sensitive method is required. The proposed system utilizes information already available to a nondestructive inspector in the form of live phased array ultrasonic images to estimate the location of the weld using non-surface, volumetric data. Data is captured using a 64-element, 10 MHz phased array probe mounted to the end-effector of a small robotic manipulator which increases the scope of applications due to its heightened flexibility when compared to on-the-market alternatives. Morphological operations are applied to the ultrasonic data to reduce the noise apparent from regions of parent material and promote the data reflected from grain boundaries within the weld material. Through a series of image processing techniques, it is possible to predict the position of a weld under inspection with an absolute mean positional error of 0.8 mm. From this study, the localization system is to be embedded within a remote system for extensive data acquisition of welds on large structures

Nesidioblastosis in the Adult Surgical Management

Nesidioblastosis is an exceedingly rare occurrence in the adult and, when it appears, it is usually part of a MEA1 syndrome

Robust search-free car number plate localization incorporating hierarchical saliency

There are two major shortcomings associated with presently implemented automatic license plate recognition (ALPR) systems: first, processing images with complex background is time-consuming and second, the results are not sufficiently accurate. To overcome these problems and also to achieve a robust recognition of multiple car number plates, saliency detection based on the ALPR system is used in this paper and also an improved and more effective definition of saliency is presented. In this new approach, the notion of the directionality of the edges using Gabor filtering and the detection of the patterns of numbers using L1 -norm have been added to the traditional saliency detection method. The proposed algorithm was tested on 660 images; some consisting of two or more cars. A detection accuracy of 94.77% and an average execution time of 40 ms for 600 × 800 images are the marked outcomes. The proposed SB-ALPR method outperforms most of the state of the art techniques in terms of execution time and accuracy, and can be used in real-time applications. Also, unlike some recently introduced saliency-based ALPR methods, our two-stage saliency detection approach exploits smaller numbers of sample sizes to reduce the computation cost