Recognition of Vehicle-Logo Based on Faster-RCNN

Huang, Zhongjie; Fu, Meixia; Ni, Kaili; Sun, Heng; Sun, Songlin

doi:10.1007/978-981-13-1733-0_10

Cited by 17 publications

(8 citation statements)

References 7 publications

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“…On the dataset VLD-30, the improved YOLOV3 produced a result of 0.899 mAP. Zhongjie Huang et al [38] was reported as the finding. The VLD-A, B, and C models of deep convolutional networks were proposed.…”

Section: Keypoint-based Methodsmentioning

confidence: 88%

Vehicle logo detection using an IoAverage loss on dataset VLD100K-61

Shi

Shen

et al. 2023

J Image Video Proc.

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Vehicle Logo Detection (VLD) is of great significance to Intelligent Transportation Systems (ITS). Although many methods have been proposed for VLD, it remains a challenging problem. To improve the VLD accuracy, an Intersection over Average (IoAverage) loss is proposed for enhancing the bounding box regression. The IoAverage loss accelerates the convergence of bounding box regression than using the Intersection over Union (IoU) loss. In the experiments, IoAverage loss has been incorporated into the state-of-the-art object detection framework YOLOV5s, namely YOLOV5s-IoAv in this paper. The advantages of the IoAverage loss are verified on the PASCAL VOC2007 datasets. The results of using the IoAverage loss show performance gains of + 15.27% mAP0.5 and + 30.87% mAP0.5:0.95 higher than that of the Complete IoU (CIoU) loss. The application of YOLOV5s-IoAv is implemented to VLD on dataset VLD100K-61. VLD100K-61 is a self-collected dataset containing 100,041 images supplied by traffic surveillance cameras in the real world from 61 categories. YOLOV5s-IoAv achieves performance gains as + 15.27% mAP0.5:0.95 for VLD than YOLOV5s-CIoU. The proposed method yields the mAP0.5 value of up to 0.992 on the dataset VLD100K-61, providing a promising solution to vehicle logo recognition applications.

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Section: Keypoint-based Methodsmentioning

confidence: 88%

Vehicle logo detection using an IoAverage loss on dataset VLD100K-61

Shi

Shen

et al. 2023

J Image Video Proc.

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show abstract

“…Three color spaces (RGB, HSV, YCbCr) are considered to encode image since these spaces are widely used for pattern recognition application. The training set is used to compute sparse score by (5) and (6). The value of these scores are then applied to rank features of training and testing set.…”

Section: Resultsmentioning

confidence: 99%

“…An enhanced logo-recognition system is presented by Psyllos et al [5] on the Medialab license plate recognition (LPR) dataset. Huang et al [6] apply Faster-RCNN model with two different CNNs (VGG-16 and ResNet-50) for vehicle logo recognition. Sotheeswaran and Ramanan [7] present a study focuses on local features that describe structural characteristics of the logo of a car using a coarse-to-fine strategy.…”

Section: Introductionmentioning

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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“…A recurrent neural network (RNN) with connectionist temporal classification (CTC) is used to label the sequence data, thus recognizing the entire LP without characterlevel segmentation. The latter, however, is sensitive to variance, especially since it is a problem of labeling on images correlated with the plates, so very small for which the neural network type RCNN [17] or Fast RCNN [13], YOLO-tiny easily fails as a result of missing of more features as well as the gathering of Arabic characters types diverges significantly this problem.…”

Section: Licenses Plates Recognition and Segmentationmentioning

confidence: 99%

An advanced combination of semi-supervised Normalizing Flow & Yolo (YoloNF) to detect and recognize vehicle license plates

Oublal¹,

Dai²

2022

Preprint

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Fully Automatic License Plate Recognition (ALPR) has been a frequent research topic due to several practical applications. However, many of the current solutions are still not robust enough in real situations, commonly depending on many constraints. This paper presents a robust and efficient ALPR system based on the state-of-the-art YOLO object detector and Normalizing flows. The model uses two new strategies: Firstly, a two-stage network using YOLO and a normalization flow-based model for normalization to detect Licenses Plates (LP) and recognize the LP with numbers and Arabic characters. Secondly, Multi-scale image transformations are implemented to provide a solution to the problem of the YOLO cropped LP detection including significant background noise. Furthermore, extensive experiments are led on a new dataset with realistic scenarios, we introduce a larger public annotated dataset collected from Moroccan plates. We demonstrate that our proposed model can learn on a small number of samples free of single or multiple characters. The dataset will also be made publicly available to encourage further studies and research on plate detection and recognition.

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Recognition of Vehicle-Logo Based on Faster-RCNN

Cited by 17 publications

References 7 publications

Vehicle logo detection using an IoAverage loss on dataset VLD100K-61

Vehicle logo detection using an IoAverage loss on dataset VLD100K-61

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

An advanced combination of semi-supervised Normalizing Flow & Yolo (YoloNF) to detect and recognize vehicle license plates

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