Next Article in Journal
Design and Manufacturing of a Disposable, Cyclo-Olefin Copolymer, Microfluidic Device for a Biosensor
Next Article in Special Issue
VisNet: Deep Convolutional Neural Networks for Forecasting Atmospheric Visibility
Previous Article in Journal
Characterization of Human Tear Fluid by Means of Surface-Enhanced Raman Spectroscopy
Previous Article in Special Issue
Deep Residual CNN-Based Ocular Recognition Based on Rough Pupil Detection in the Images by NIR Camera Sensor
Open AccessArticle

Multi-Oriented and Scale-Invariant License Plate Detection Based on Convolutional Neural Networks

by Jing Han 1, Jian Yao 1,*, Jiao Zhao 1,2, Jingmin Tu 1 and Yahui Liu 1
1
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430070, China
2
School of Sociology, Wuhan University, Wuhan 430070, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(5), 1175; https://doi.org/10.3390/s19051175
Received: 8 January 2019 / Revised: 3 March 2019 / Accepted: 4 March 2019 / Published: 7 March 2019
(This article belongs to the Special Issue Deep Learning-Based Image Sensors)
License plate detection (LPD) is the first and key step in license plate recognition. State-of-the-art object-detection algorithms based on deep learning provide a promising form of LPD. However, there still exist two main challenges. First, existing methods often enclose objects with horizontal rectangles. However, horizontal rectangles are not always suitable since license plates in images are multi-oriented, reflected by rotation and perspective distortion. Second, the scale of license plates often varies, leading to the difficulty of multi-scale detection. To address the aforementioned problems, we propose a novel method of multi-oriented and scale-invariant license plate detection (MOSI-LPD) based on convolutional neural networks. Our MOSI-LPD tightly encloses the multi-oriented license plates with bounding parallelograms, regardless of the license plate scales. To obtain bounding parallelograms, we first parameterize the edge points of license plates by relative positions. Next, we design mapping functions between oriented regions and horizontal proposals. Then, we enforce the symmetry constraints in the loss function and train the model with a multi-task loss. Finally, we map region proposals to three edge points of a nearby license plate, and infer the fourth point to form bounding parallelograms. To achieve scale invariance, we first design anchor boxes based on inherent shapes of license plates. Next, we search different layers to generate region proposals with multiple scales. Finally, we up-sample the last layer and combine proposal features extracted from different layers to recognize true license plates. Experimental results have demonstrated that the proposed method outperforms existing approaches in terms of detecting license plates with different orientations and multiple scales. View Full-Text
Keywords: convolutional neural networks; deep learning; license plate detection; multi-orientation; multi-scale detection convolutional neural networks; deep learning; license plate detection; multi-orientation; multi-scale detection
Show Figures

Figure 1

MDPI and ACS Style

Han, J.; Yao, J.; Zhao, J.; Tu, J.; Liu, Y. Multi-Oriented and Scale-Invariant License Plate Detection Based on Convolutional Neural Networks. Sensors 2019, 19, 1175.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop