CNN for license plate motion deblurring

Svoboda, Pavel; Hradiš, Michal; Marsik, Lukas; Zemčík, Pavel

doi:10.1109/icip.2016.7533077

Cited by 56 publications

(36 citation statements)

References 19 publications

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“…Some papers focus on deblurring the LPs, which is very useful for LP recognition. Lu et al [34] proposed a scheme based on sparse representation to identify the blur kernel, while Svoboda et al [35] employed a text deblurring CNN for reconstruction of blurred LPs. Despite achieving exceptional qualitative results, the additional computational cost of a deblurring stage usually is prohibitive for realtime ALPR applications.…”

Section: License Plate Recognitionmentioning

confidence: 99%

A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

Laroca

Severo

Zanlorensi

et al. 2018

2018 International Joint Conference on Neural Networks (IJCNN)

419

271

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In this paper, we present an efficient and layout-independent Automatic License Plate Recognition (ALPR) system based on the stateof-the-art YOLO object detector that contains a unified approach for license plate (LP) detection and layout classification to improve the recognition results using post-processing rules. The system is conceived by evaluating and optimizing different models with various modifications, aiming at achieving the best speed/accuracy trade-off at each stage. The networks are trained using images from several datasets, with the addition of various data augmentation techniques, so that they are robust under different conditions. The proposed system achieved an average end-to-end recognition rate of 96.8% across eight public datasets (from five different regions) used in the experiments, outperforming both previous works and commercial systems in the ChineseLP, OpenALPR-EU, SSIG-SegPlate and UFPR-ALPR datasets. In the other datasets, the proposed approach achieved competitive results to those attained by the baselines. Our system also achieved impressive frames per second (FPS) rates on a high-end GPU, being able to perform in real time even when there are four vehicles in the scene. An additional contribution is that we manually labeled 38,351 bounding boxes on 6,239 images from public datasets and made the annotations publicly available to the research community.

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Section: License Plate Recognitionmentioning

confidence: 99%

A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

Laroca

Severo

Zanlorensi

et al. 2018

2018 International Joint Conference on Neural Networks (IJCNN)

419

271

View full text Add to dashboard Cite

show abstract

“…Jaderberg et al used a large word dictionary and formulated the text recognition task as a large scale classification problem. Recently, Svoboda et al [5] used CNN to remove motion blur from images of license plate blurred with a blur kernel of various directions and lengths. Although this approach is able to deblur highly blurred images, it does not contend with extremely low resolution and noisy images.…”

Section: Introductionmentioning

confidence: 99%

Deciphering Severely Degraded License Plates

Agarwal¹,

Tran²,

Torresani³

et al. 2017

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Extremely low-quality images, on the order of 20 pixels in width, appear with frustrating frequency in many forensic investigations. Even advanced de-noising and super-resolution technologies are unable to extract useful information from such lowquality images. We show, however, that useful information is present in such highly degraded images. We also show that convolutional neural networks can be trained to decipher the contents of highly degraded images of license plates, and that these networks significantly outperform human observers.

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“…Traditional stateof-the-art methods such as Sun et al [26] or Michaeli and Irani [16] use carefully chosen patch-based priors for sharp image prediction. Data-driven methods based on neural networks have demonstrated success in non-blind restoration tasks [21,31,20] as well as for the more challenging task of BD where the blur kernel is unknown [22,25,2,10,27]. Removing the blur from moving objects has been recently addressed in [17].…”

Section: Related Workmentioning

confidence: 99%

Learning Blind Motion Deblurring

Wieschollek

Hirsch

Schölkopf

et al. 2017

2017 IEEE International Conference on Computer Vision (ICCV)

127

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previous methods our result evious methods our result lt Figure 1. From a sequence of blurry inputs (lower row) our learning-based approach for blind burst-deblurring reconstructs fine details, which are not recovered by recent state-of-the-art methods like FBA [6]. Both methods feature similar run-time. AbstractAs handheld video cameras are now commonplace and available in every smartphone, images and videos can be recorded almost everywhere at anytime. However, taking a quick shot frequently yields a blurry result due to unwanted camera shake during recording or moving objects in the scene. Removing these artifacts from the blurry recordings is a highly ill-posed problem as neither the sharp image nor the motion blur kernel is known. Propagating information between multiple consecutive blurry observations can help restore the desired sharp image or video. In this work, we propose an efficient approach to produce a significant amount of realistic training data and introduce a novel recurrent network architecture to deblur frames taking temporal information into account, which can efficiently handle arbitrary spatial and temporal input sizes.

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CNN for license plate motion deblurring

Cited by 56 publications

References 19 publications

A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector

Deciphering Severely Degraded License Plates

Learning Blind Motion Deblurring

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