TY - JOUR
T1 - Automatic License Plate Recognition in real-world traffic videos captured in unconstrained environment by a mobile camera
AU - Khan, I.R.
AU - Ali, S.T.A.
AU - Siddiq, A.
AU - Khan, M.M.
AU - Ilyas, M.U.
AU - Alshomrani, S.
AU - Rahardja, S.
PY - 2022/4/28
Y1 - 2022/4/28
N2 - Automatic License Plate Recognition (ALPR) has remained an active research topic for years due to various applications, especially in Intelligent Transportation Systems (ITS). This paper presents an efficient ALPR technique based on deep learning, which accurately performs license plate (LP) recognition tasks in an unconstrained environment, even when trained on a limited dataset. We capture real traffic videos in the city and label the LPs and the alphanumeric characters in the LPs within different frames to generate training and testing datasets. Data augmentation techniques are applied to increase the number of training and testing samples. We apply the transfer learning approach to train the recently released YOLOv5 object detecting framework to detect the LPs and the alphanumerics. Next, we train a convolutional neural network (CNN) to recognize the detected alphanumerics. The proposed technique achieved a recognition rate of 92.8% on a challenging proprietary dataset collected in several jurisdictions of Saudi Arabia. This accuracy is higher than what was achieved on the same dataset by commercially available Sighthound (86%), PlateRecognizer (67%), OpenALPR (77%), and a state-of-the-art recent CNN model (82%). The proposed system also outperformed the existing ALPR solutions on several benchmark datasets
AB - Automatic License Plate Recognition (ALPR) has remained an active research topic for years due to various applications, especially in Intelligent Transportation Systems (ITS). This paper presents an efficient ALPR technique based on deep learning, which accurately performs license plate (LP) recognition tasks in an unconstrained environment, even when trained on a limited dataset. We capture real traffic videos in the city and label the LPs and the alphanumeric characters in the LPs within different frames to generate training and testing datasets. Data augmentation techniques are applied to increase the number of training and testing samples. We apply the transfer learning approach to train the recently released YOLOv5 object detecting framework to detect the LPs and the alphanumerics. Next, we train a convolutional neural network (CNN) to recognize the detected alphanumerics. The proposed technique achieved a recognition rate of 92.8% on a challenging proprietary dataset collected in several jurisdictions of Saudi Arabia. This accuracy is higher than what was achieved on the same dataset by commercially available Sighthound (86%), PlateRecognizer (67%), OpenALPR (77%), and a state-of-the-art recent CNN model (82%). The proposed system also outperformed the existing ALPR solutions on several benchmark datasets
UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-85129193703&partnerID=MN8TOARS
U2 - 10.3390/electronics11091408
DO - 10.3390/electronics11091408
M3 - Article
SN - 2079-9292
VL - 11
JO - Electronics (Switzerland)
JF - Electronics (Switzerland)
IS - 9
M1 - 1408
ER -