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

Laroca, Rayson; Severo, Evair; Zanlorensi, Luiz A.; Oliveira, Luiz S.; Gonçalves, Gabriel Resende; Schwartz, William Robson; Menotti, David

doi:10.1109/ijcnn.2018.8489629

Cited by 434 publications

(307 citation statements)

References 54 publications

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“…Then, we use 416 × 416 images as input since the best results (speed/accuracy trade-off in the validation set) were obtained with this dimension as input. It is remarkable that, although YOLO networks have a 1 : 1 input aspect ratio, previous works 15,30 have attained excellent object detection results (over 99% recall) in images with different aspect ratios (e.g., 1,920 × 1,080). All image resizing operations were performed using bilinear interpolation.…”

Section: Counter Detectionmentioning

confidence: 96%

Convolutional neural networks for automatic meter reading

et al. 2019

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In this paper, we tackle Automatic Meter Reading (AMR) by leveraging the high capability of Convolutional Neural Networks (CNNs). We design a two-stage approach that employs the Fast-YOLO object detector for counter detection and evaluates three different CNN-based approaches for counter recognition. In the AMR literature, most datasets are not available to the research community since the images belong to a service company. In this sense, we introduce a new public dataset, called UFPR-AMR dataset, with 2,000 fully and manually annotated images. This dataset is, to the best of our knowledge, three times larger than the largest public dataset found in the literature and contains a well-defined evaluation protocol to assist the development and evaluation of AMR methods. Furthermore, we propose the use of a data augmentation technique to generate a balanced training set with many more examples to train the CNN models for counter recognition. In the proposed dataset, impressive results were obtained and a detailed speed/accuracy trade-off evaluation of each model was performed. In a public dataset, state-of-the-art results were achieved using less than 200 images for training.

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Section: Counter Detectionmentioning

confidence: 96%

Convolutional neural networks for automatic meter reading

et al. 2019

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“…Our approach shows superior performance to other LPR algorithms on LPR accuracy and image recovery. Furthermore, we achieve comparable results with state-of-the-art LPR method [18,35]. From Table 4, our method obtains the highest performance (93.08%), and outperforms the state-of-the-art methods by more than 5.74% (87.34% vs 93.08%).…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 56%

SNIDER: Single Noisy Image Denoising and Rectification for Improving License Plate Recognition

Lee

Ahn

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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In this paper, we present an algorithm for real-world license plate recognition (LPR) from a low-quality image. Our method is built upon a framework that includes denoising and rectification, and each task is conducted by Convolutional Neural Networks. Existing denoising and rectification have been treated separately as a single network in previous research. In contrast to the previous work, we here propose an end-to-end trainable network for image recovery, Single Noisy Image DEnoising and Rectification (SNIDER), which focuses on solving both the problems jointly. It overcomes those obstacles by designing a novel network to address the denoising and rectification jointly. Moreover, we propose a way to leverage optimization with the auxiliary tasks for multi-task fitting and novel training losses. Extensive experiments on two challenging LPR datasets demonstrate the effectiveness of our proposed method in recovering the high-quality license plate image from the low-quality one and show that the the proposed method outperforms other state-of-the-art methods.

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“…Also, it is important to note that, under the fairly tilted conditions, our method operated consistently robust and successfully detects the characters, while the baseline fail to detect. Furthermore, one interesting finding of these results is that, based on Figure 6 (b,c), the addition of adversarial loss lead to the highlighting of the positive features, Figure 5: Example in GIST-LP dataset (Laroca et al, 2018). Qualitative sample images of recognition results.…”

Section: Comparison With Other Methodsmentioning

confidence: 87%

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

Cited by 434 publications

References 54 publications

Convolutional neural networks for automatic meter reading

Convolutional neural networks for automatic meter reading

SNIDER: Single Noisy Image Denoising and Rectification for Improving License Plate Recognition

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

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