Real-time Bhutanese license plate localization using YOLO

Jamtsho, Yonten; Riyamongkol, Panomkhawn; Waranusast, Rattapoom

doi:10.1016/j.icte.2019.11.001

Cited by 42 publications

(26 citation statements)

References 5 publications

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“…The bounding box results are then used with feature extraction using a CNN [ 36 , 37 , 38 ] for classification and regression. Typical vehicle LPR involve the You Only Look Once (YOLO) series [ 39 , 40 , 41 , 42 , 43 , 44 ] and single shot detector [ 45 ]. In most studies, YOLO models have exhibited high accuracy and recall, both above 98% [ 31 , 40 , 46 , 47 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning

Chou

Liu

2021

Sensors

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Sand theft or illegal mining in river dredging areas has been a problem in recent decades. For this reason, increasing the use of artificial intelligence in dredging areas, building automated monitoring systems, and reducing human involvement can effectively deter crime and lighten the workload of security guards. In this investigation, a smart dredging construction site system was developed using automated techniques that were arranged to be suitable to various areas. The aim in the initial period of the smart dredging construction was to automate the audit work at the control point, which manages trucks in river dredging areas. Images of dump trucks entering the control point were captured using monitoring equipment in the construction area. The obtained images and the deep learning technique, YOLOv3, were used to detect the positions of the vehicle license plates. Framed images of the vehicle license plates were captured and were used as input in an image classification model, C-CNN-L3, to identify the number of characters on the license plate. Based on the classification results, the images of the vehicle license plates were transmitted to a text recognition model, R-CNN-L3, that corresponded to the characters of the license plate. Finally, the models of each stage were integrated into a real-time truck license plate recognition (TLPR) system; the single character recognition rate was 97.59%, the overall recognition rate was 93.73%, and the speed was 0.3271 s/image. The TLPR system reduces the labor force and time spent to identify the license plates, effectively reducing the probability of crime and increasing the transparency, automation, and efficiency of the frontline personnel’s work. The TLPR is the first step toward an automated operation to manage trucks at the control point. The subsequent and ongoing development of system functions can advance dredging operations toward the goal of being a smart construction site. By intending to facilitate an intelligent and highly efficient management system of dredging-related departments by providing a vehicle LPR system, this paper forms a contribution to the current body of knowledge in the sense that it presents an objective approach for the TLPR system.

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Section: Literature Reviewmentioning

confidence: 99%

Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning

Chou

Liu

2021

Sensors

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“…In visual data, object detection is a technique to find a candidate region for a detection target to recognize a specific target and to predict the type and location of the object (bounding box) [18,19]. The algorithms for this include R-CNN [20], Fast-R-CNN [21], Faster-R-CNN [22], and YOLO [23]. R-CNN (Regions with Convolutional Neural Networks) is divided into three stages to detect objects.…”

Section: Image Object Detection Algorithmmentioning

confidence: 99%

“…It is also easy to understand what objects exist in the image and where they are located by learning the data only once. Since YOLO learns the general characteristics of objects, it can be predicted even when new data is entered [23]. Accordingly, S. Lu et al [24] proposed a real-time object detection algorithm in the video.…”

Section: Appl Sci 2020 10 X For Peer Review 3 Of 19mentioning

confidence: 99%

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Pothole Classification Model Using Edge Detection in Road Image

Baek

Chung

2020

Applied Sciences

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Since the image related to road damage includes objects such as potholes, cracks, shadows, and lanes, there is a problem that it is difficult to detect a specific object. In this paper, we propose a pothole classification model using edge detection in road image. The proposed method converts RGB (red green and blue) image data, including potholes and other objects, to gray-scale to reduce the amount of computation. It detects all objects except potholes using an object detection algorithm. The detected object is removed, and a pixel value of 255 is assigned to process it as a background. In addition, to extract the characteristics of a pothole, the contour of the pothole is extracted through edge detection. Finally, potholes are detected and classified based by the (you only look once) YOLO algorithm. The performance evaluation evaluates the distortion rate and restoration rate of the image, and the validity of the model and accuracy of the classification. The result of the evaluation shows that the mean square error (MSE) of the distortion rate and restoration rate of the proposed method has errors of 0.2–0.44. The peak signal to noise ratio (PSNR) is evaluated as 50 db or higher. The structural similarity index map (SSIM) is evaluated as 0.71–0.82. In addition, the result of the pothole classification shows that the area under curve (AUC) is evaluated as 0.9.

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“…Many factors affect ALPR recognition accuracies such as light ambient, image resolution, camera angles, etc. Previously in ALPR researches has been done, there were 2 methods Generally used such as morphological approach [2][3][4][5] and Deep Learning approach [6][7][8][9]. The deep learning approach is often used recently because has better accuracy than the edge detection approach, Despite the fact deep learning approach has poor performance speed.…”

Section: Introductionmentioning

confidence: 99%

Indonesian Plate Number Identification Using YOLACT and Mobilenetv2 in the Parking Management System

et al. 2021

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A vehicle registration plate is used for vehicle identity. In recent years, technology to identify plate numbers automatically or known as Automatic License Plate Recognition (ALPR) has grown over time. Convolutional Neural Network and YOLACT are used to do plate number recognition from a video. The number plate recognition process consists of 3 stages. The first stage determines the coordinates of the number plate area on a video frame using YOLACT. The second stage is to separate each character inside the plat number using morphological operations, horizontal projection, and topological structural. The third stage is recognizing each character candidate using CNN MobileNetV2. To reduce computation time by only take several frames in the video, frame sampling is performed. This experiment study uses frame sampling, YOLACT epoch, MobileNet V2 epoch, and the ratio of validation data as parameters. The best results are with 250ms frame sampling succeed to reduce computational times up to 78%, whereas the accuracy is affected by the MobileNetV2 model with 100 epoch and ratio of split data validation 0,1 which results in 83,33% in average accuracy. Frame sampling can reduce computational time however higher frame sampling value causes the system fails to obtain plate region area.

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Real-time Bhutanese license plate localization using YOLO

Cited by 42 publications

References 5 publications

Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning

Automated Sensing System for Real-Time Recognition of Trucks in River Dredging Areas Using Computer Vision and Convolutional Deep Learning

Pothole Classification Model Using Edge Detection in Road Image

Indonesian Plate Number Identification Using YOLACT and Mobilenetv2 in the Parking Management System

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