An All-in-One Vehicle Type and License Plate Recognition System Using YOLOv4

Abstract

In smart surveillance and urban mobility applications, camera-equipped embedded platforms with deep learning technology have demonstrated applicability and effectiveness in identifying various targets. These use cases can be found in a variety of contexts and locations. It is critical to collect relevant data from the location where the application will be deployed. In this paper, we propose an integrated vehicle type and license plate recognition system using YOLOv4, which consists of vehicle type detection, license plate detection, and license plate character detection to better support the context of Korean vehicles in multilane highway and urban environments. Using our dataset of one to four multilane images, our system detected six vehicle classes and license plates with mAP of 98.0%, 94.0%, 97.1%, and 84.6%, respectively. On our dataset and a publicly available open dataset, our system demonstrated mAP of 99.3% and 99.4% for the detected license plates, respectively. From 4K high-resolution images, our system was able to detect minuscule license plates as small as 100 pixels wide. We believe that our system can be used in densely populated regions to address the high demands for enhanced visual sensitivity in smart cities and Internet-of-Things.

A License Plate Recognition System with Robustness against Adverse Environmental Conditions Using Hopfield’s Neural Network

“…Moreover, the accuracy of the proposed algorithm was higher than a number of similar ones developed on SOTA [15,16]. On the other hand, the time spent on recognizing the characters of each license plate was almost equal to the time spent on recognizing only one character in methods based on convolutional neural networks [15][16][17][18].…”

“…In tasks based on image processing, such as car license plate recognition [15][16][17][18] or eye tracking, etc., the first step is usually to determine the approximate location of the target object. For this, a large number of different cars with different license plate locations were studied.…”

“…Moreover, the accuracy of the proposed algorithm was higher than a number of similar ones developed on SOTA [15,16]. On the other hand, the time spent on recognizing the characters of each license plate was almost equal to the time spent on recognizing only one character in methods based on convolutional neural networks [15][16][17][18]. To determine the accuracy of Hopfield's neural network in determining numbers and letters, according to Figure 12, a set of different car license plates was considered.…”

Car Parking Slot Detection Using Yolo Algorithm

“…In [7] a system using YOLOv4 integrates Korean License Plate Recognition with vehicle type detection for smart city applications. The methodology involves deep learning and open-source frameworks to train YOLOv4 for custom class detection.…”

Multiple Mechanisms to Strengthen the Ability of YOLOv5s for Real-Time Identification of Vehicle Type

“…However, their method can only detect vehicles in images acquired from fixed monitoring equipment and not in images obtained by on-board monitoring equipment. Park et al [9] developed an algorithmic framework that can simultaneously detect the type of vehicle and its license plate information using the YOLOV4 algorithm, and created a dataset of types of vehicles. However, their algorithm has stringent requirements on the performance of the equipment used, and cannot satisfy the real-time requirements of Jetson AGX.…”