Fast vehicle logo detection in complex scenes

Yang, Shuo; Zhang, Junxing; Bo, Chunjuan; Wang, Meng; Chen, Lijun

doi:10.1016/j.optlastec.2018.08.007

Cited by 42 publications

(18 citation statements)

References 24 publications

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“…First, we calculate the average value Raver, Gaver, and Baver of the brightness value of the white reference point. Then calculate the gain of each channel according to Equations (5) The function of white point detection is to enhance the robustness of the algorithm. The algorithm flow is as follows:…”

Section: Dynamic Threshold Methods Of White Balancementioning

confidence: 99%

“…Because of the influence of light, there are many noise points in binary images, so how to reduce noise filtering is the most important part of this algorithm. For example, a hybrid license plate character segmentation algorithm is proposed in the paper [5], combining the license plate character segmentation of connected areas and the character segmentation based on conditional random field, so as to solve the problem that the traditional license plate character segmentation is difficult to solve low image quality license plate. The improved high-low cap transformation is used in the paper [6] to optimize the traditional fixed threshold binarization algorithm, and it is applied to the binarization algorithm of license plate image to moderate the effect of uneven light on license plate image.…”

Section: Image Classificationmentioning

confidence: 99%

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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: Dynamic Threshold Methods Of White Balancementioning

confidence: 99%

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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“…Liu et al extend the unsupervised sparsity score to supervised context by utilizing the class label information [36,37]. Let denotes the ℎ feature of ℎ instance in class , ̂ is the element of sparse similarity matrix which is constructed within the class , is a Ndimensional vector with =1, if belongs to the class and 0 otherwise.…”

Section: Feature Selectionmentioning

confidence: 99%

Vehicle logo recognition using histograms of oriented gradient descriptor and sparsity score

2020

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Most of vehicle have the similar structures and designs. It is extremely complicated and difficult to identify and classify vehicle brands based on their structure and shape. As we require a quick and reliable response, so vehicle logos are an alternative method of determining the type of a vehicle. In this paper, we propose a method for vehicle logo recognition based on feature selection method in a hybrid way. Vehicle logo images are first characterized by Histograms of Oriented Gradient descriptors and the final features vector are then applied feature selection method to reduce the irrelevant information. Moreover, we release a new benchmark dataset for vehicle logo recognition and retrieval task namely, VLR-40. The experimental results are evaluated on this database which show the efficiency of the proposed approach.

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“…Common network models include VGG16, GoogleNet and DarkNet [24]. The YOLO algorithm is based on the DarkNet network.…”

Section: Crop Identification and Feature Point Locationmentioning

confidence: 99%

Extraction and Research of Crop Feature Points Based on Computer Vision

Cui

Zhang

Sun

et al. 2019

Sensors

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Based on computer vision technology, this paper proposes a method for identifying and locating crops in order to successfully capture crops in the process of automatic crop picking. This method innovatively combines the YOLOv3 algorithm under the DarkNet framework with the point cloud image coordinate matching method, and can achieve the goal of this paper very well. Firstly, RGB (RGB is the color representing the three channels of red, green and blue) images and depth images are obtained by using the Kinect v2 depth camera. Secondly, the YOLOv3 algorithm is used to identify the various types of target crops in the RGB images, and the feature points of the target crops are determined. Finally, the 3D coordinates of the feature points are displayed on the point cloud images. Compared with other methods, this method of crop identification has high accuracy and small positioning error, which lays a good foundation for the subsequent harvesting of crops using mechanical arms. In summary, the method used in this paper can be considered effective.

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Fast vehicle logo detection in complex scenes

Cited by 42 publications

References 24 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

Vehicle logo recognition using histograms of oriented gradient descriptor and sparsity score

Extraction and Research of Crop Feature Points Based on Computer Vision

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