Accurate License Plate Recognition and Super-Resolution Using a Generative Adversarial Networks on Traffic Surveillance Video

Lee, Younkwan; Yun, Jae-Woong; Hong, Yoojin; Lee, Juhyun; Jeon, Moongu

doi:10.1109/icce-asia.2018.8552121

Cited by 36 publications

(16 citation statements)

References 10 publications

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“…Vasek et al [21] proposed the convolutional neural network (CNN) method for recognizing license plates in very LR videos. Lee et al [22] proposed the SR method based on generative adversarial networks that can be applied in the license plate recognition challenged environments. However, these methods are not collaborative learning for character recognition, and they just connected parallel the SR network and character recognition.…”

Section: Combination Of Sr With Other Task-driven Neural Networkmentioning

confidence: 99%

Alternative Collaborative Learning for Character Recognition in Low-Resolution Images

2022

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Character recognition in a single image is a technology utilized in various sensor platforms, such as smart parking and text-to-speech systems, and numerous studies are being conducted to improve its performance by experimenting with novel approaches. However, when low-quality images were inputted to a character recognition neural network for recognition, a difference in the resolution of the training image and low-quality image results in poor accuracy. To resolve this problem, this study proposes a collaborative trainable neural network that integrates a super-resolution neural network with a character recognition neural network such that the proposed collaborative trainable neural network is robust to inputs with varying quality in the real world. An alternative collaborative learning and character recognition performance test was conducted using the license plate image dataset among various character images, and the effectiveness of the proposed algorithm was verified using a performance test.

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Section: Combination Of Sr With Other Task-driven Neural Networkmentioning

confidence: 99%

Alternative Collaborative Learning for Character Recognition in Low-Resolution Images

2022

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“…e block diagram below represents the working of the same. TTS is built using python [29]. Python facilitates with different APIs to convert text to speech.…”

Section: Merits and Limitationsmentioning

confidence: 99%

Auditory Speech Based Alerting System for Detecting Dummy Number Plate via Video Processing Data sets

Deshpande

Veena

Ferede

2022

Computational Intelligence and Neuroscience

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Spectrum of applications in computer vision use object detection algorithms driven by the power of AI and ML algorithms. State of art detection models like faster Region based convolutional Neural Network (RCNN), Single Shot Multibox Detector (SSD), and You Only Look Once (YOLO) demonstrated a good performance for object detection, but many failed in detecting small objects. In view of this an improved network structure of YOLOv4 is proposed in this paper. This work presents an algorithm for small object detection trained using real-time high-resolution data for porting it on embedded platforms. License plate recognition, which is a small object in a car image, is considered for detection and an auditory speech signal is generated for detecting fake license plates. The proposed network is improved in the following aspects: Training the classifier by using positive data set formed from the core patterns of an image. Training YOLOv4 by the features obtained by decomposing the image into low frequency and high frequency. The resultant values are processed and demonstrated via a speech alerting signals and messages. This contributes to reducing the computation load and increasing the accuracy. Algorithm was tested on eight real-time video data sets. The results show that our proposed method greatly reduces computing effort while maintaining comparable accuracy. It takes 45 fps to detect one image when the input size is 1280 × 960, which could keep a real-time speed. Proposed algorithm works well in case of tilted, blurred, and occluded license plates. Also, an auditory traffic monitoring system can reduce criminal attacks by detecting suspicious license plates. The proposed algorithm is highly applicable for autonomous driving applications.

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“…e.g,. generative focused tasks; super-resolution (Nguyen et al, ;Ledig et al, 2017;Lee et al, 2018), style transfer (Zhu et al, 2017;Li et al, 2017), natural-language processing (Rajeswar et al, 2017) and discriminative focused tasks; human pose estimation (Chou et al, 2017;Peng et al, 2018).…”

Section: Adversarial Learningmentioning

confidence: 99%

Practical License Plate Recognition in Unconstrained Surveillance Systems with Adversarial Super-Resolution

Lee

Jun

Hong

et al. 2019

Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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Although most current license plate (LP) recognition applications have been significantly advanced, they are still limited to ideal environments where training data are carefully annotated with constrained scenes. In this paper, we propose a novel license plate recognition method to handle unconstrained real world traffic scenes. To overcome these difficulties, we use adversarial super-resolution (SR), and one-stage character segmentation and recognition. Combined with a deep convolutional network based on VGG-net, our method provides simple but reasonable training procedure. Moreover, we introduce GIST-LP, a challenging LP dataset where image samples are effectively collected from unconstrained surveillance scenes. Experimental results on AOLP and GIST-LP dataset illustrate that our method, without any scene-specific adaptation, outperforms current LP recognition approaches in accuracy and provides visual enhancement in our SR results that are easier to understand than original data.

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Accurate License Plate Recognition and Super-Resolution Using a Generative Adversarial Networks on Traffic Surveillance Video

Cited by 36 publications

References 10 publications

Alternative Collaborative Learning for Character Recognition in Low-Resolution Images

Alternative Collaborative Learning for Character Recognition in Low-Resolution Images

Auditory Speech Based Alerting System for Detecting Dummy Number Plate via Video Processing Data sets

Practical License Plate Recognition in Unconstrained Surveillance Systems with Adversarial Super-Resolution

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