Deep corner prediction to rectify tilted license plate images
| Autor: | Hojin Yoo, Kyungkoo Jun |
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| Rok vydání: | 2020 |
| Předmět: |
Ground truth
Artificial neural network Computer Networks and Communications Computer science business.industry Feature extraction 020207 software engineering 02 engineering and technology Object detection Euclidean distance Transformation (function) Hardware and Architecture Feature (computer vision) 0202 electrical engineering electronic engineering information engineering Media Technology 020201 artificial intelligence & image processing Computer vision Artificial intelligence business Software Information Systems Homography (computer vision) |
| Zdroj: | Multimedia Systems. 27:779-786 |
| ISSN: | 1432-1882 0942-4962 |
| Popis: | Skewness and obliqueness of vehicle plate images influence license plate recognition. The more tilted plate images are, the harder the recognition task is. To this end, if plate images are preprocessed to be aligned and rectified, the recognition performance would improve. We propose deep neural network models that can locate four corner plate positions, which can then be used to perform the perspective transformation that can be used to rectify plates. Such a transformation is called homography. The models consist of two sequential parts: a feature extraction part having convolution and a regression part with fully connected layers. The models are open in the sense that the feature extraction part can host other well-known models such as Mobilenet as long as they have the feature capture capability. We devise a loss function as the sum of Euclidean distance between predicted coordinates and ground truth and discuss image augmentation schemes. The experiment results show that the models with well-known object detection models are able to predict corner positions with relatively high precision. |
| Databáze: | OpenAIRE |
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