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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Chunling Tu
Tshwane University of Technology
Computer Science & IT Electrical & Electronics Engineering

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A hierarchical RCNN for vehicle and vehicle license plate detection and recognition Chunling Tu; Shengzhi Du
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp731-737

Abstract

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.
Hybrid order characteristics in car-following behavior Chunling Tu; Shengzhi Du
Indonesian Journal of Electrical Engineering and Computer Science Vol 20, No 1: October 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v20.i1.pp158-166

Abstract

This paper addresses the discovery of an interesting property in car-following processes, which was not reported in the existing literatures. A hybrid order behavior is supported by both experimental data and theoretical simulations. To demonstrate this behavior, the first order and the second order car-following behaviors are defined. Then, by comparing the first and the second order car-following behaviors in the existing analystic models and the real traffic context, this paper finds that a significant amount of the second order car-following processes in real traffic context do not match the existing models and structural mismatches are observed. The popularity and significance of such cases suggest the existence of unmodelled dynamics in the existing methods, that is, the car following behavior should be determined by more factors than the immediate proceeding vehicle. Therefore, the existing car-following models must be improved to accommodate these factors. This forms one of the main values of this paper. This paper then defines the hybrid order car-following behavior and prompts to associate this behavior with the concerned unmodelled dynamics (mismatches between the actual traffic data and the simulation from models). The neural network is employed to model such dynamics. The idea of the proposed hybrid order behavior matches the fact that the car-following behavior is determined by multiple vehicles driving in front of the subject car instead of only the immediate proceeding one. This is valuable because it provides guidance on the improvement of existing car-following models. The neural network model validates that the consideration of multiple vehicles improves the accuracy of car-following modelling.
A chaos-based medical image encryption method Xuehong Wang; Chunling Tu
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i3.pp1316-1324

Abstract

The information in the e-health system involves the patient’s privacy which is extremely sensitive. For instance, these information record social security numbers and detailed medical history.  When the breach happens, there are illegal, or disclosure behaviors taken to privacy that should be compromised security. In this paper, the medical digital images are protected by the proposed chaos-based encryption method in the process of transmission and utilization. Then the authentication is granted to patients and telemedicine staffs by the decryption method proposed. The Qi 3-D four wing chaotic system is employed in this method. The proposed method promises the keys with higher complexity and unpredictability than traditional cryptography methods by introducing the chaotic dynamics in the new cryptography. Digital medical images are used to validate the proposed method under the brute and the differential attacks. The pixels of the image are scrambled completely based on cat map and the sub-blocks of the image are diffused in the way that the original image is changed into a chaotic image robust to all kinds of attacks. The experiments show that the proposed method has higher performance and higher computation for decryption.
Co-Authors Shengzhi Du Xuehong Wang