Automatic Jordanian License Plate Detection and Recognition System Using Deep Learning Techniques.

Recently, the number of vehicles on the road, especially in urban centres, has increased dramatically due to the increasing trend of individuals towards urbanisation. As a result, manual detection and recognition of vehicles (i.e., license plates and vehicle manufacturers) become an arduous task and beyond human capabilities. In this paper, we have developed a system using transfer learning-based deep learning (DL) techniques to identify Jordanian vehicles automatically. The YOLOv3 (You Only Look Once) model was re-trained using transfer learning to accomplish license plate detection, character recognition, and vehicle logo detection. In contrast, the VGG16 (Visual Geometry Group) model was re-trained to accomplish the vehicle logo recognition. To train and test these models, four datasets have been collected. The first dataset consists of 7035 Jordanian vehicle images, the second dataset consists of 7176 Jordanian license plates, and the third dataset consists of 8271 Jordanian vehicle images. These datasets have been used to train and test the YOLOv3 model for Jordanian license plate detection, character recognition, and vehicle logo detection. In comparison, the fourth dataset consists of 158,230 vehicle logo images used to train and test the VGG16 model for vehicle logo recognition. Text measures were used to evaluate the performance of our developed system. Moreover, the mean average precision (mAP) measure was used to assess the YOLOv3 model of the detection tasks (i.e., license plate detection and vehicle logo detection). For license plate detection, the precision, recall, F-measure, and mAP were 99.6%, 100%, 99.8%, and 99.9%, respectively. While for character recognition, the precision, recall, and F-measure were 100%, 99.9%, and 99.95%, respectively. The performance of the license plate recognition stage was evaluated by evaluating these two sub-stages as a sequence, where the precision, recall, and F-measure were 99.8%, 99.8%, and 99.8%, respectively. Furthermore, for vehicle logo detection, the precision, recall, F-measure, and mAP were 99%, 99.6%, 99.3%, and 99.1%, respectively, while for vehicle logo recognition, the precision, recall, and F-measure were 98%, 98%, and 98%, respectively. The performance of the vehicle logo recognition stage was evaluated by evaluating these two sub-stages as a sequence, where the precision, recall, and F-measure were 95.3%, 99.5%, and 97.4%, respectively.