TY  - JOUR
VL  - 22
A1  - Mahmood, Z.
A1  - Khan, K.
A1  - Khan, U.
A1  - Adil, S.H.
A1  - Ali, S.S.A.
A1  - Shahzad, M.
JF  - Sensors
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124101001&doi=10.3390%2fs22031245&partnerID=40&md5=8a7c0e6363bd0274f8eedb4e0b676efe
PB  - MDPI
SN  - 14248220
Y1  - 2022///
TI  - Towards Automatic License Plate Detection
ID  - scholars17116
KW  - Image segmentation; Intelligent systems; Intelligent vehicle highway systems; Object detection; Optical character recognition; Tracking (position)
KW  -  Critical steps; Input image; Integral components; Intelligent transportation systems; License plate detection; License plate localizations; Object Tracking; Segmentation; Vehicle license plates; Vehicles detection
KW  -  License plates (automobile)
KW  -  algorithm; image processing; intelligence; methodology
KW  -  Algorithms; Image Processing
KW  -  Computer-Assisted; Intelligence; Research Design
N1  - cited By 10
N2  - Automatic License Plate Detection (ALPD) is an integral component of using computer vision approaches in Intelligent Transportation Systems (ITS). An accurate detection of vehiclesâ?? license plates in images is a critical step that has a substantial impact on any ALPD systemâ??s recognition rate. In this paper, we develop an efficient license plate detecting technique through the intelligent combination of Faster R-CNN along with digital image processing techniques. The proposed algorithm initially detects vehicle(s) in the input image through Faster R-CNN. Later, the located vehicle is analyzed by a robust License Plate Localization Module (LPLM). The LPLM module primarily uses color segmentation and processes the HSV image to detect the license plate in the input image. Moreover, the LPLM module employs morphological filtering and dimension analysis to find the license plate. Detailed trials on challenging PKU datasets demonstrate that the proposed method outperforms few recently developed methods by producing high license plates detection accuracy in much less execution time. The proposed work demonstrates a great feasibility for security and target detection applications. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
IS  - 3
AV  - none
ER  -