An End-to-End License Plate Localization and Recognition System

An end-to-end license plate recognition (LPR) system is proposed. It is composed of pre-processing, detection, segmentation and character recognition to find and recognize plates from camera based still images. The system utilizes connected component (CC) properties to quickly extract the license plate region. A novel two-stage CC filtering is utilized to address both shape and spatial relationship information to produce high precision and recall values for detection. Floating peak and valleys (FPV) of projection profiles are used to cut the license plates into individual characters. A turning function based method is proposed to recognize each character quickly and accurately. It is further accelerated using curvature histogram based support vector machine (SVM). The INFTY dataset is used to train the recognition system. And MediaLab license plate dataset is used for testing. The proposed system achieved 89.45% F-measure for detection and 87.33% accuracy for overall recognition rate which is comparable to current state-of-the-art systems

Automated Data Digitization System for Vehicle Registration Certificates Using Google Cloud Vision API

This study aims to develop an automated data digitization system for the Thai vehicle registration certificate. It is the first system developed as a web service Application Programming Interface (API), which is essential for any enterprise to increase its business value. Currently, this system is available on “www.carjaidee.com”. The system involves four steps: 1) an embedded frame aligns a document to be correctly recognised in the image acquisition step; 2) sharpening and brightness filtering techniques to enhance image quality are applied in the pre-processing step; 3) the Google Cloud Vision API receives a prompt to proceed in the recognition step; 4) a specific domain dictionary to improve accuracy rate is developed for the post-processing step. This study defines 92 images for the experiment by counting the correct words and terms from the output. The findings suggest that the proposed method, which had an average accuracy of 93.28%, was significantly more accurate than the original method using only the Google Cloud Vision API. However, the system is limited because the dictionaries cannot automatically recognise a new word. In the future, we will explore solutions to this problem using natural language processing techniques. Doi: 10.28991/CEJ-2022-08-07-09 Full Text: PD

Visual Analysis Algorithms for Embedded Systems

The main contribution of this thesis is the design and development of an optimized framework to realize the deep neural classifiers on the embedded platforms. Deep convolutional networks exhibit unmatched performance in image classification. However, these deep classifiers demand huge computational power and memory storage. That is an issue on embedded devices due to limited onboard resources. The computational demand of neural networks mainly stems from the convolutional layers. A significant improvement in performance can be obtained by reducing the computational complexity of these convolutional layers, making them realizable on embedded platforms. In this thesis, we proposed a CUDA (Compute Unified Device Architecture)-based accelerated scheme to realize the deep architectures on the embedded platforms by exploiting the already trained networks. All required functions and layers to replicate the trained neural networks were implemented and accelerated using concurrent resources of embedded GPU. Performance of our CUDA-based proposed scheme was significantly improved by performing convolutions in the transform domain. This matrix multiplication based convolution was also compared with the traditional approach to analyze the improvement in inference performance. The second part of this thesis focused on the optimization of the proposed framework. The flow of our CUDA-based framework was optimized using unified memory scheme and hardware-dependent utilization of computational resources. The proposed flow was evaluated over three different image classification networks on Jetson TX1 embedded board and Nvidia Shield K1 tablet. The performance of proposed GPU-only flow was compared with its sequential and heterogeneous versions. The results showed that the proposed scheme brought the higher performance and enabled the real-time image classification on the embedded platforms with lesser storage requirements. These results motivated us towards the realization of useful real-time classification and recognition problems on the embedded platforms. Finally, we utilized the proposed framework to realize the neural network-based automatic license plate recognition (ALPR) system on a mobile platform. This highly-precise and computationally demanding system was deployed by simplifying the flow of trained deep architecture developed for powerful desktop and server environments. A comparative analysis of computational complexity, recognition accuracy and inference performance was performed

Errors and Truths from Transportation Data Aggregation: Some Implications for Research and Practice

Data aggregation, which is a process to combine information by defined groups for statistical analysis, summary, data size reduction, or other purposes, has fundamental challenges, such as loss of the original information. Improper data aggregation, such as sampling bias or incorrect calculation of average, may cause misreading of information. In first chapter, it is revealed that the harmonic mean, which is used to calculate space mean speed for fixed segment, has a sampling bias, i.e., overestimation with small samples. The several impact analyses show that the sampling bias is affected by sampling rate, time interval, segment length, and distribution type. If the data aggregation is properly used, it can help us improve analytical efficiency, encounter some of critical problems, or reveal its casualties and other relevant information. Second and third chapters utilize the aggregation of multi-source data to estimate error distributions of data sources and improve accuracy of their measurements. This is a leaping point of evaluating data sources as the proposed model does not require ground truth data. Second chapter focuses more on the methodology, i.e., a modified Approximate Bayesian Computation, incorporated to construct the error distribution with numerous simulations. In the simulated experiment, the proposed model outperformed the alternative approach, which is a conventional way of evaluating data source that is gathering error information by comparing with ground data source. Several sensitivity analyses explore that how the model performance is affected by sample size, number of data sources, and distribution types. The proposed model in chapter II is limited to one dimensional variable, and then the application is expanded to improving the position and distance measurement of connected vehicle environment. The proposed model can be used to further improve the accuracy of vehicle positioning with other existing methods, such as simultaneous localization and mapping (SLAM). The estimation process can be conducted in real-time operation, and the learning process will try to keep improving the accuracy of estimation. The results show that the proposed model noticeably improves the accuracy of position and distance measurements

Defect Detection in Arc-Welding Processes by Means of the Line-to-Continuum Method and Feature Selection

Plasma optical spectroscopy is widely employed in on-line welding diagnostics. The determination of the plasma electron temperature, which is typically selected as the output monitoring parameter, implies the identification of the atomic emission lines. As a consequence, additional processing stages are required with a direct impact on the real time performance of the technique. The line-to-continuum method is a feasible alternative spectroscopic approach and it is particularly interesting in terms of its computational efficiency. However, the monitoring signal highly depends on the chosen emission line. In this paper, a feature selection methodology is proposed to solve the uncertainty regarding the selection of the optimum spectral band, which allows the employment of the line-to-continuum method for on-line welding diagnostics. Field test results have been conducted to demonstrate the feasibility of the solution

Potentialities of the combined use of underwater fluorescence imagery and photogrammetry for the detection of fine-scale changes in marine bioconstructors

Marine communities are facing both natural disturbances and anthropogenic stressors. Bioconstructor species are endangered by multiple large-scale and local pressures and the early identification of impacts and damages is a primary goal for preserving coral reefs. Taking advantage of the recent development in underwater photogrammetry, the use of photogrammetry and fluorimetry was coupled to design, test and validate in laboratory a multi-sensor measuring system that could be potentially exploited in open water by SCUBA divers for assessing the health status of corals and detecting relevant biometric parameters with high accuracy and resolution. The approach was tested with fragments of the endemic coral Cladocora caespitosa, the sole zooxanthellate scleractinian reef-builder in the Mediterranean. The most significant results contributing to the scientific advancement of knowledge were: 1) the development of a cost-effective, flexible and easy-to-use approach based on emerging technologies; 2) the achievement of a sub-centimetric resolution for measuring relevant biometric parameters (polyp counting, colony surface areas and volumes); 3) set up of a reliable and repeatable strategy for multi-temporal analyses capable of quantifying changes in coral morphology with sub-centimeter accuracy; 4) detect changes in coral health status at a fine scale and under natural lighting through autofluorescence analysis. The novelty of the present research lies in the coupling of emerging techniques that could be applied to a wide range of 3D morphometrics, different habitats and species, thus paving the way to innovative opportunities in ecological research and more effective results than traditional in-situ measurements. Moreover, the possibility to easily modify the developed system to be installed on an underwater remotely operated vehicle further highlights the possible concrete impact of the research for ecological monitoring and protection purposes

Optical granulometric analysis of sedimentary deposits by color segmentation-based software: OPTGRAN-CS

The study of grain size distribution is fundamental for understanding sedimentological environments. Through these analyses, clast erosion, transport and deposition processes can be interpreted and modeled. However, grain size distribution analysis can be difficult in some outcrops due to the number and complexity of the arrangement of clasts and matrix and their physical size. Despite various technological advances, it is almost impossible to get the full grain size distribution (blocks to sand grain size) with a single method or instrument of analysis. For this reason development in this area continues to be fundamental. In recent years, various methods of particle size analysis by automatic image processing have been developed, due to their potential advantages with respect to classical ones; speed and final detailed content of information (virtually for each analyzed particle). In this framework, we have developed a novel algorithm and software for grain size distribution analysis, based on color image segmentation using an entropy-controlled quadratic Markov measure field algorithm and the Rosiwal method for counting intersections between clast and linear transects in the images. We test the novel algorithm in different sedimentary deposit types from 14 varieties of sedimentological environments. The results of the new algorithm were compared with grain counts performed manually by the same Rosiwal methods applied by experts. The new algorithm has the same accuracy as a classical manual count process, but the application of this innovative methodology is much easier and dramatically less time-consuming. The final productivity of the new software for analysis of clasts deposits after recording field outcrop images can be increased significantly.The study of grain size distribution is fundamental for understanding sedimentological environments. Through these analyses, clast erosion, transport and deposition processes can be interpreted and modeled. However, grain size distribution analysis can be difficult in some outcrops due to the number and complexity of the arrangement of clasts and matrix and their physical size. Despite various technological advances, it is almost impossible to get the full grain size distribution (blocks to sand grain size) with a single method or instrument of analysis. For this reason development in this area continues to be fundamental. In recent years, various methods of particle size analysis by automatic image processing have been developed, due to their potential advantages with respect to classical ones; speed and final detailed content of information (virtually for each analyzed particle). In this framework, we have developed a novel algorithm and software for grain size distribution analysis, based on color image segmentation using an entropy-controlled quadratic Markov measure field algorithm and the Rosiwal method for counting intersections between clast and linear transects in the images. We test the novel algorithm in different sedimentary deposit types from 14 varieties of sedimentological environments. The results of the new algorithm were compared with grain counts performed manually by the same Rosiwal methods applied by experts. The new algorithm has the same accuracy as a classical manual count process, but the application of this innovative methodology is much easier and dramatically less time-consuming. The final productivity of the new software for analysis of clasts deposits after recording field outcrop images can be increased significantly