TY - GEN
T1 - A Robust Method to Locate License Plates under Diverse Conditions
AU - Awan, Sheryar Mehmood
AU - Khattak, Shadan
AU - Khan, Gul Zameen
AU - Mahmood, Zahid
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10/3
Y1 - 2019/10/3
N2 - Automatic License Plate Detection (ALPD) is a crucial step, which significantly affects the recognition rate and speed of the Intelligent Transport System (ITS). This paper presents a robust license plate detection method using an intelligent combination of Faster R-CNN and image processing operations. In the proposed method, initially, a vehicle is detected in the input colored RGB images using the Faster R-CNN. Next, the image with detected vehicle is fed to our developed License Plate Localization Module (LPLM) to search the possible existence of the license plate. The LPLM converts the detected vehicle image from RGB to the HSV domain and applies color segmentation along with morphological operations, and finally uses the dimensions analysis to locate the license plate. Simulations on the challenging PKU dataset reveal that the proposed technique outperforms recent state-of-the-art methods in terms of detection accuracy, precision, recall, and execution time.
AB - Automatic License Plate Detection (ALPD) is a crucial step, which significantly affects the recognition rate and speed of the Intelligent Transport System (ITS). This paper presents a robust license plate detection method using an intelligent combination of Faster R-CNN and image processing operations. In the proposed method, initially, a vehicle is detected in the input colored RGB images using the Faster R-CNN. Next, the image with detected vehicle is fed to our developed License Plate Localization Module (LPLM) to search the possible existence of the license plate. The LPLM converts the detected vehicle image from RGB to the HSV domain and applies color segmentation along with morphological operations, and finally uses the dimensions analysis to locate the license plate. Simulations on the challenging PKU dataset reveal that the proposed technique outperforms recent state-of-the-art methods in terms of detection accuracy, precision, recall, and execution time.
KW - Intelligent Transport Systems
KW - Machine Learning
KW - Object Detection
UR - http://www.scopus.com/inward/record.url?scp=85073682757&partnerID=8YFLogxK
U2 - 10.1109/ICAEM.2019.8853773
DO - 10.1109/ICAEM.2019.8853773
M3 - Conference contribution
AN - SCOPUS:85073682757
T3 - 2019 International Conference on Applied and Engineering Mathematics, ICAEM 2019 - Proceedings
SP - 92
EP - 98
BT - 2019 International Conference on Applied and Engineering Mathematics, ICAEM 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Conference on Applied and Engineering Mathematics, ICAEM 2019
Y2 - 27 August 2019 through 29 August 2019
ER -