Vehicle plate number localization using a modified GrabCut algorithm

Salau, Ayodeji Olalekan; Yesufu, Thomas Kokumo; Ogundare, B. S.

doi:10.1016/j.jksuci.2019.01.011

Cited by 35 publications

(13 citation statements)

References 33 publications

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“…Logic and operation are performed with binary images generated based on HIS (Hue, Saturation and Intensity) space. The image enhancement method is firstly used in the paper [8] to increase the image contrast and weaken the effect of uneven light on positioning, and then the Canny arithmetic operators are used to detect the image edge. Although the above methods for noise reduction filtering of binary image improve the accuracy of character segmentation to a certain extent, they have little significance for license plate area detection and vehicle face recognition, especially after the advent of deep learning correlation algorithms.…”

Section: Image Classificationmentioning

confidence: 99%

Research on Image Adaptive Enhancement Algorithm under Low Light in License Plate Recognition System

Shi

Yuan

2020

Symmetry

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The traffic block port monitors and manages the road traffic by shooting and recording the motor vehicles. However, due to the complex factors such as shooting angle, light condition, environmental background, etc., the recognition rate of license plate is not high enough. High light and low light under complex lighting conditions are symmetry problems. This paper analyzes and solves the low light problem in detail, an image adaptive enhancement algorithm under low light conditions is proposed in the paper. The algorithm mainly includes four modules, among which, the fast image classification module uses the deep and separable convolutional neural network to classify low-light images into low-light images by day and low-light images by night, greatly reducing the computation burden on the basis of ensuring the classification accuracy. The image enhancement module inputs the classified images into two different image enhancement algorithms and adopts the idea of dividing and ruling; the image quality evaluation module adopts a weighted comprehensive evaluation index. The final experiment shows that the comprehensive evaluation indexes are all greater than 0.83, which can improve the subsequent recognition of vehicle face and license plate.

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Section: Image Classificationmentioning

confidence: 99%

Research on Image Adaptive Enhancement Algorithm under Low Light in License Plate Recognition System

Shi

Yuan

2020

Symmetry

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“…For the process of license plate localization, researchers have proposed various methods including, connected component analysis (CCA) [ 1 ], morphological analysis with edge statistics [ 2 ], edge point analysis [ 3 ], color processing [ 4 ], and deep learning [ 5 ]. The rate of accuracy for these localization methods varies from 80.00% to 99.80% [ 3 , 6 , 7 ]. The methods most commonly used for the recognition stage are optical character recognition (OCR) [ 8 , 9 ], template matching [ 10 , 11 ], feature extraction and classification [ 12 , 13 ], and deep learning based methods [ 5 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

A Vision-Based Machine Learning Method for Barrier Access Control Using Vehicle License Plate Authentication

Islam

Raj

Islam

et al. 2020

Sensors

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Automatic vehicle license plate recognition is an essential part of intelligent vehicle access control and monitoring systems. With the increasing number of vehicles, it is important that an effective real-time system for automated license plate recognition is developed. Computer vision techniques are typically used for this task. However, it remains a challenging problem, as both high accuracy and low processing time are required in such a system. Here, we propose a method for license plate recognition that seeks to find a balance between these two requirements. The proposed method consists of two stages: detection and recognition. In the detection stage, the image is processed so that a region of interest is identified. In the recognition stage, features are extracted from the region of interest using the histogram of oriented gradients method. These features are then used to train an artificial neural network to identify characters in the license plate. Experimental results show that the proposed method achieves a high level of accuracy as well as low processing time when compared to existing methods, indicating that it is suitable for real-time applications.

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“…Traditional image segmentation methods include supervised methods, unsupervised methods, and interactive methods [12][13][14], among which interactive methods can achieve better segmentation results than other methods. As an interactive method, the GrabCut algorithm has been widely used because of its simple interactivity and satisfactory image segmentation results [15][16][17][18][19]. It has been applied to resolve different segmentation problems, such as medical computerized tomography (CT) and Positron Emission Computed Tomography (PET) image segmentation [16,17], human face segmentation [18], vehicle plate number recognition [19], and building extraction [20].…”

Section: Introductionmentioning

confidence: 99%

“…As an interactive method, the GrabCut algorithm has been widely used because of its simple interactivity and satisfactory image segmentation results [15][16][17][18][19]. It has been applied to resolve different segmentation problems, such as medical computerized tomography (CT) and Positron Emission Computed Tomography (PET) image segmentation [16,17], human face segmentation [18], vehicle plate number recognition [19], and building extraction [20]. Until now, few studies have been performed using GrabCut for mining area segmentation with high-resolution remote sensing images.…”

Section: Introductionmentioning

confidence: 99%

An Improved GrabCut Method Based on a Visual Attention Model for Rare-Earth Ore Mining Area Recognition with High-Resolution Remote Sensing Images

Yan

Zhang

et al. 2019

Remote Sensing

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An improved GrabCut method based on a visual attention model is proposed to extract rare-earth ore mining area information using high-resolution remote sensing images. The proposed method makes use of advantages of both the visual attention model and GrabCut method, and the visual attention model was referenced to generate a saliency map as the initial of the GrabCut method instead of manual initialization. Normalized Difference Vegetation Index (NDVI) was designed as a bound term added into the Energy Function of GrabCut to further improve the accuracy of the segmentation result. The proposed approach was employed to extract rare-earth ore mining areas in Dingnan County and Xunwu County, China, using GF-1 (GaoFen No.1 satellite launched by China) and ALOS (Advanced Land Observation Satellite) high-resolution remotely-sensed satellite data, and experimental results showed that FPR (False Positive Rate) and FNR (False Negative Rate) were, respectively, lower than 12.5% and 6.5%, and PA (Pixel Accuracy), MPA (Mean Pixel Accuracy), MIoU (Mean Intersection over Union), and FWIoU (frequency weighted intersection over union) all reached up to 90% in four experiments. Comparison results with traditional classification methods (such as Object-oriented CART (Classification and Regression Tree) and Object-oriented SVM (Support Vector Machine)) indicated the proposed method performed better for object boundary identification. The proposed method could be useful for accurate and automatic information extraction for rare-earth ore mining areas.

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Vehicle plate number localization using a modified GrabCut algorithm

Cited by 35 publications

References 33 publications

Research on Image Adaptive Enhancement Algorithm under Low Light in License Plate Recognition System

Research on Image Adaptive Enhancement Algorithm under Low Light in License Plate Recognition System

A Vision-Based Machine Learning Method for Barrier Access Control Using Vehicle License Plate Authentication

An Improved GrabCut Method Based on a Visual Attention Model for Rare-Earth Ore Mining Area Recognition with High-Resolution Remote Sensing Images

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