Predicted Anchor Region Proposal with Balanced Feature Pyramid for License Plate Detection in Traffic Scene Images

Nguyen, Hoanh

doi:10.1155/2020/5137056

Cited by 3 publications

(6 citation statements)

References 32 publications

(73 reference statements)

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“…It is obvious from Table 5 that these recent methods using deep networks achieve better detection accuracy than methods using hand-crafted features. Comparison results in Table 5 show that the proposed method obtains comparable detection accuracy on all subsets compared to other deep CNN-based methods (i.e., methods proposed by Li et al [8] and Nguyen [9]). However, the proposed model significantly improves the detection speed.…”

Section: Detection Results On Pku Vehicle Datasetmentioning

confidence: 94%

“…For PKU vehicle dataset, this paper compares the detection results of different license plate methods as shown in Table 5. In Table 5, methods proposed by Li et al [8] and Nguyen [9] are based on deep networks, while methods designed by Zhou et al [5], Li et al [6], and Yuan et al [7] employ handcrafted features and a traditional classifier to locate license plates. It is obvious from Table 5 that these recent methods using deep networks achieve better detection accuracy than methods using hand-crafted features.…”

Section: Detection Results On Pku Vehicle Datasetmentioning

confidence: 99%

“…However, the proposed model significantly improves the detection speed. Specifically, the proposed model takes 72 ms for processing an image, while methods proposed by Nguyen [9] requires 420 ms. Method proposed by Yuan et al [7] is faster than the proposed method since it uses simple linear SVMs.…”

Section: Detection Results On Pku Vehicle Datasetmentioning

confidence: 99%

“…Experimental results show that the Fast-LPRNet achieves high detection accuracy with fast speed. In [9], a license plate detection method based on predicted anchor region proposal network and balanced feature pyramid network was proposed. e predicted anchor region proposal network employs predicted location anchor scheme to generate high-quality sparse proposal boxes.…”

Section: Related Workmentioning

confidence: 99%

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An Efficient License Plate Detection Approach Using Lightweight Deep Convolutional Neural Networks

Nguyen

2022

Advances in Multimedia

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Benefited from deep convolutional neural networks, various license plate detection methods based on deep networks have been proposed and achieved significant improvements compared with traditional methods. However, the high computational cost due to complex structures prevents these methods from being deployed in real-world applications. This paper proposes an efficient license plate detection method based on lightweight deep convolutional neural networks for improving the detection speed. To extract high-level features from input images, this paper designs a lightweight feature pyramid generation module based on a lightweight architecture and depth-wise convolutions. To further enhance feature pyramid, an efficient feature enhancement module is designed to fuse features generated by the region proposal network with backbone features. In the detection network, a light head structure based on fully connected layers is employed to further reduce the computational cost of the model. In experiments, floating point operations and detection ratio are used to evaluate the efficient of the proposed method. Experimental results on public datasets show that the proposed method achieves the best trade-off between speed and accuracy.

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Section: Detection Results On Pku Vehicle Datasetmentioning

confidence: 94%

Section: Detection Results On Pku Vehicle Datasetmentioning

confidence: 99%

Section: Detection Results On Pku Vehicle Datasetmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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An Efficient License Plate Detection Approach Using Lightweight Deep Convolutional Neural Networks

Nguyen

2022

Advances in Multimedia

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“…In the task of license plate detection, Nguyen et al designed the anchors of fixed length and proportion at different layers of the feature pyramid according to the aspect ratio specified by the license plate. They used a multiscale regional proposal network with predicted anchor position information to finally improve the detection effect [ 38 ]. Ma et al considered the spatial relationship between anchor, ground truth, and bounding box for a one-stage anchor-based object detection algorithm.…”

Section: Related Workmentioning

confidence: 99%

Research on Object Detection of PCB Assembly Scene Based on Effective Receptive Field Anchor Allocation

Chen

et al. 2022

Computational Intelligence and Neuroscience

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Vision-based object detection of PCB (printed circuit board) assembly scenes is essential in accelerating the intelligent production of electronic products. In particular, it is necessary to improve the detection accuracy as much as possible to ensure the quality of assembly products. However, the lack of object detection datasets in PCB assembly scenes is the key to restricting intellectual PCB assembly research development. As an excellent representative of the one-stage object detection model, YOLOv3 (you only look once version 3) mainly relies on placing predefined anchors on the three feature pyramid layers and realizes recognition and positioning using regression. However, the number of anchors distributed in each grid cell of different scale feature layers is usually the same. The ERF (effective receptive field) corresponding to the grid cell at different locations varies. The contradiction between the uniform distribution of fixed-size anchors and the ERF size range in different feature layers will reduce the effectiveness of object detection. Few people use ERF as a standard for assigning anchors to improve detection accuracy. To address this issue, firstly, we constructed a PCB assembly scene object detection dataset, which includes 21 classes of detection objects in three scenes before assembly, during assembly, and after assembly. Secondly, we performed a refined ERF analysis on each grid of the three output layers of YOLOv3, determined the ERF range of each layer, and proposed an anchor allocation rule based on the ERF. Finally, for the small and difficult-to-detect TH (through-holes), we increased the context information and designed improved-ASPP (Atrous spatial pyramid pooling) and channel attention joint module. Through a series of experiments on the object detection dataset of the PCB assembly scene, we found that under the framework of YOLOv3, anchor allocation based on ERF can increase mAP (mean average precision) from 79.32% to 89.86%. At the same time, our proposed method is superior to Faster R-CNN (region convolution neural network), SSD (single shot multibox detector), and YOLOv4 (you only look once version 4) in the balance of high detection accuracy and low computational complexity.

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A High-Performance Approach for Irregular License Plate Recognition in Unconstrained Scenarios

Nguyen¹

2023

IJACSA

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This paper proposes a novel framework for locating and recognizing irregular license plates in real-world complex scene images. In the proposed framework, an efficient deep convolutional neural network (CNN) structure specially designed for keypoint estimation is first employed to predict the corner points of license plates. Then, based on the predicted corner points, perspective transformation is performed to align the detected license plates. Finally, a lightweight deep CNN structure based on the YOLO detector is designed to predict license plate characters. The character recognition network can predict license plate characters without depending on license plate layouts (i.e., license plates of single-line or double-line text). Experiment results on CCPD and AOLP datasets demonstrate that the proposed method obtains better recognition accuracy compared with previous methods. The proposed model also achieves impressive inference speed and can be deployed in realtime applications.

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Predicted Anchor Region Proposal with Balanced Feature Pyramid for License Plate Detection in Traffic Scene Images

Cited by 3 publications

References 32 publications

An Efficient License Plate Detection Approach Using Lightweight Deep Convolutional Neural Networks

An Efficient License Plate Detection Approach Using Lightweight Deep Convolutional Neural Networks

Research on Object Detection of PCB Assembly Scene Based on Effective Receptive Field Anchor Allocation

A High-Performance Approach for Irregular License Plate Recognition in Unconstrained Scenarios

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