scholarly journals Fast Cartoon-Texture Decomposition Filtering Based License Plate Detection Method

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Yingjun Wang ◽  
Chenping Zhao ◽  
Xiaoyan Liu ◽  
Mingfu Zhao ◽  
Linfeng Bai
Keyword(s):  
High Efficiency ◽  
Detection Method ◽  
Correction Method ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
License Plate ◽  
License Plate Recognition ◽  
Background Interference ◽  
Morphological Filtering ◽  
License Plate Detection

Vehicle license plate detection is an important step in automatic license plate recognition, which is prone to be influenced by the background interference and complex environment conditions. It is known that cartoon-texture decomposition split an image into geometric cartoon and texture component, which can remove background interference away from the vehicle image. In this paper, we introduce a fast cartoon-texture decomposition filter into the detection process. Combining the edge detection, morphological filtering and Radon transform based tilt correction method, we formulate a new license plate detection algorithm. Experiment results confirm that the proposed algorithm can remove background interference away, inhibit the emergence of fake license plates, and improve the detection accuracy. Moreover, there is no inner loop iteration in the new algorithm, so it is fast and high-efficiency.

Detection optimization of license plate targets based on AdvancedEAST

2021 ◽  
pp. 1-9
Author(s):  
Feifei Yin ◽  
Jingxuan Wang ◽  
Wei Xiong ◽  
Juanjuan Gao ◽  
Yu Gong
Keyword(s):  
Transfer Learning ◽  
Traffic Management ◽  
Recognition Rate ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Natural Scenes ◽  
License Plate ◽  
License Plate Recognition ◽  
Detection Model ◽  
License Plate Detection

As an important core in the intelligent traffic management system, the technology and application of license plate recognition have become research focus. Detecting the accurate location of a license plate from a vehicle image is considered to be the most crucial step of license plate recognition, which greatly affects the recognition rate and speed of the whole system. Nevertheless, due to the low accuracy of license plate detection in natural scenes, further investigations are still needed in this field in order to make the detection process very efficient. In this paper, We have studied and implemented a convolutional neural network license plate detection algorithm based on transfer learning. According to the invention, new energy license plates and ordinary license plates are adopted as the research objects. The text detection model AdvancedEAST is trained on the license plate images through the transfer learning method, and experiments are carried out on the self-built license plate dataset. The experimental results show that the algorithm can better adapt to light complexity, low resolution, target interference, license plate tilt and other complex conditions. The license plate positioning algorithm has high accuracy in natural scenes, and it is superior to the traditional license plate detection methods.

A Configurable Tilt License Plate Correction Method Based on Parallel Lines

2013 ◽  
Vol 385-386 ◽  
pp. 1429-1433 ◽  
Author(s):  
Zhong Yan Liang ◽  
San Yuan Zhang
Keyword(s):  
Recognition System ◽  
Correction Method ◽  
Selection Method ◽  
Experimental Results ◽  
License Plate ◽  
License Plate Recognition ◽  
Parallel Lines ◽  
Bounding Box ◽  
Bounding Boxes ◽  
Straight Lines

The tilt license plate correction is an important part of the license plate recognition system. Traditional correction methods are based on one theory. It is difficult to use the advantages of different approaches. We propose some methods to help improve the tile license plate correction: a bounding box selection method based on similar height and a mutual correction method based on fitted parallel straight lines. Moreover, we use wide bounding boxes to segment touched characters. If the method based on parallel lines fails, another method, such as PCA-based one, can be used for complement. Experimental results show the proposed method outperforms others.

Robot Collision Detection Without External Sensors Based on Time-Series Analysis

2020 ◽  
Vol 143 (4) ◽  
Author(s):  
Tie Zhang ◽  
Peizhong Ge ◽  
Yanbiao Zou ◽  
Yingwu He
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Collision Detection ◽  
Degrees Of Freedom ◽  
Detection Method ◽  
Industrial Robot ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
External Torque ◽  
Series Analysis

Abstract To ensure the human safety in the process of human–robot cooperation, this paper proposes a robot collision detection method without external sensors based on time-series analysis (TSA). In the investigation, first, based on the characteristics of the external torque of the robot, the internal variation of the external torque sequence during the movement of the robot is analyzed. Next, a time-series model of the external torque is constructed, which is used to predict the external torque according to the historical motion information of the robot and generate a dynamic threshold. Then, the detailed process of time-series analysis for collision detection is described. Finally, the real-machine experiment scheme of the proposed real-time collision detection algorithm is designed and is used to perform experiments with a six degrees-of-freedom (6DOF) articulated industrial robot. The results show that the proposed method helps to obtain a detection accuracy of 100%; and that, as compared with the existing collision detection method based on a fixed symmetric threshold, the proposed method based on TSA possesses smaller detection delay and is more feasible in eliminating the sensitivity difference of collision detection in different directions.

scholarly journals An Improved Jacobi-Based Detector for Massive MIMO Systems

Information ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 165 ◽  
Author(s):  
Xiaoqing Zhao ◽  
Zhengquan Li ◽  
Song Xing ◽  
Yang Liu ◽  
Qiong Wu ◽  
...  
Keyword(s):  
Massive Mimo ◽  
Communication Systems ◽  
Mimo Systems ◽  
Minimum Mean Square Error ◽  
Multiple Input Multiple Output ◽  
Correction Method ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Input Multiple Output ◽  
Mmse Detector

Massive multiple-input-multiple-output (MIMO) is one of the key technologies in the fifth generation (5G) cellular communication systems. For uplink massive MIMO systems, the typical linear detection such as minimum mean square error (MMSE) presents a near-optimal performance. Due to the required direct matrix inverse, however, the MMSE detection algorithm becomes computationally very expensive, especially when the number of users is large. For achieving the high detection accuracy as well as reducing the computational complexity in massive MIMO systems, we propose an improved Jacobi iterative algorithm by accelerating the convergence rate in the signal detection process.Specifically, the steepest descent (SD) method is utilized to achieve an efficient searching direction. Then, the whole-correction method is applied to update the iterative process. As the result, the fast convergence and the low computationally complexity of the proposed Jacobi-based algorithm are obtained and proved. Simulation results also demonstrate that the proposed algorithm performs better than the conventional algorithms in terms of the bit error rate (BER) and achieves a near-optimal detection accuracy as the typical MMSE detector, but utilizing a small number of iterations.

License Plate Detection and Facial Analysis Using TensorFlow Deep Learning Algorithm

2019 ◽  
Vol 16 (8) ◽  
pp. 3246-3251
Author(s):  
P. Manoj Prakash ◽  
Sreerag Premanathan ◽  
ShivamKumar Surwase ◽  
M. S. Antony Vigil ◽  
Shivam Bohare
Keyword(s):  
Learning Algorithm ◽  
Ccd Camera ◽  
License Plate ◽  
License Plate Recognition ◽  
Facial Analysis ◽  
Deep Learning Algorithm ◽  
Canny Edge ◽  
License Plate Detection ◽  
Toll Collection ◽  
Improved Technology

Nowadays license plate recognition has been applied in car access control, toll collection and other applications. Even though they exist, car thefts and illegal use of other proprietor’s license plate remain a problem. To deal with this, a computational programmed controlled framework is being developed. Also, facial analysis algorithm is implemented so as to create awareness among the common public. The way forward is to use an improved technology combination of License Plate Detection and Facial Analysis using artificial intelligence, in which vehicle image is captured by high resolution CCD camera and the license plate region is determined by image processing algorithms and facial analysis is done by using FaceNet and TensorFlow. The characters of the license plate is separated by segmentation and processed using the Canny Edge and Blob Coloring algorithm and the facial analysis is done using Facenet of TensorFlow.

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