Neural Network for Denoising and Reading Degraded License Plates
| Autor: | Gianmaria Rossi, Marco Fontani, Simone Milani |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
Computer science Character (computing) business.industry Deep learning Noise reduction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition 02 engineering and technology Convolutional neural network Visual inspection Image denoising License plates Face (geometry) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business License |
| Zdroj: | Pattern Recognition. ICPR International Workshops and Challenges ISBN: 9783030687793 ICPR Workshops (6) |
| Popis: | The denoising and the interpretation of severely-degraded license plates is one of the main problems that law enforcement agencies face worldwide and everyday. In this paper, we present a system made by coupling two convolutional neural networks. The first one produces a denoised version of the input image; the second one takes the denoised and original images to estimate a prediction of each character in the plate. Considering the complexity of gathering training data for this task, we propose a way of creating and augmenting an artificial dataset, which also allows tailoring the training to the specific license plate format of a given country at little cost. The system is designed as a tool to aid law enforcement investigations when dealing with low resolution corrupted license plates. Compared to existing methods, our system provides both a denoised license plate and a prediction of the characters to enable a visual inspection and an accurate validation of the final result. We validated the system on a dataset of real license plates, yielding a sensible perceptual improvement and an average character classification accuracy of 93%. |
| Databáze: | OpenAIRE |
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