Development of ANPR Framework for Pakistani Vehicle Number Plates Using Object Detection and OCR

Salma,; Saeed, Maham; Rahim, Rauf ur; Khan, Muhammad Gufran; Zulfiqar, Adil; Bhatti, Muhammad Tahir

doi:10.1155/2021/5597337

Cited by 17 publications

(3 citation statements)

References 25 publications

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“…Therefore, all images were resized to a size of 640 x 640. YOLO has been used in extracting information from tables [136], [137], [138]; license plate recognition [139], [140], automated invoice parsing [141], and for automated meter reading [142]. Instead of learning regions like in a Faster R-CNN, YOLO (currently in its seventh version) looks at the complete image, splits it into n x n grids, then uses a single CNN to predict the bounding boxes and the class probabilities for these boxes [143].…”

Section: You Only Look Once (Yolo)mentioning

confidence: 99%

Enhanced safety implementation in 5S + 1 via object detection algorithms

Shahin

Chen

Hosseinzadeh

et al. 2023

Int J Adv Manuf Technol

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Scholarly work points to 5S+1, a simple yet powerful method of initiating quality in manufacturing, as one of the foundations of Lean manufacturing and the Toyota Production Systems. The 6 th S, safety, is often used to prevent future occupational hazards, therefore, reducing the loss of time, money, and human resources. This paper aims to show how Industry 4.0 technologies such as computer-based vision and object detection algorithms can help implement the 6 th S in 5S+1 through monitoring and detecting workers who fail to adhere to standard safety practices such as wearing Personal Protective Equipment (PPE).The paper evaluated and analyzed three different detection approaches and compared their performance metrics. In total, seven models were proposed to perform such a task. All the proposed models utilized You-Only-Look-Once (YOLO v7) architecture to verify workers' PPE compliance. In approach I, three models were used to detect workers, safety helmets and safety vests. Then, a machine learning algorithm was used to verify if each detected worker is in PPE compliance. In approach II, the model simultaneously detects individual workers and veri es PPE compliance. In approach III, three different models were used to detect workers in the input feed. Then, a deep learning algorithm was used to verify the safety. All models were trained on Pictor-v3 dataset. It is found that the third approach, when utilizing VGG-16 algorithm, achieves the best performance, i.e., 80% F1 score, and can process 11.79 Frames per Second (FPS), making it suitable for real-time detection.

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Section: You Only Look Once (Yolo)mentioning

confidence: 99%

Enhanced safety implementation in 5S + 1 via object detection algorithms

Shahin

Chen

Hosseinzadeh

et al. 2023

Int J Adv Manuf Technol

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“…The findings show that license plate detection and recognition are both 98.22% and 78% accurate, respectively. Salma et al [6] proposed localising the position of the licence plate using the YOLOv3 and YOLOv4 device locators. To recognize the plate label, use Tesseract's optical character recognition (OCR) technology.…”

Section: Introductionmentioning

confidence: 99%

Vehicle number recognition by using existing general surveillance cameras

Sharmila,

Jerin,

Kumar

et al. 2024

E3S Web Conf.

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India has a lot of cars because it is the densest-populated country in the world. Therefore, it's important to use a traffic management system to detect vehicles accurately. It detects a vehicle plate image from a camera and extracts the number. It's an embedded system that recognizes license plates in real-time. A real-time embedded system called Vehicle Plate Number Recognition (VNPR) recognizes license plate numbers automatically in this work using VGG16 algorithm. When a vehicle enters the entrance by the gate of the institution or highly restricted areas, the system records and captures video and recognizes the number plate. It will automatically recognize the license plate and search the data of the owner's vehicle and their challan details.

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“…Therefore, in an attempt to resolve this issue, refs. [ 12 , 13 , 14 , 15 , 16 ] proposed algorithms to distinguish vehicles based on shapes, size, traveling speed, and distance from camera views. However, the algorithms disregarded the vehicle’s visual information and resulted in poor overall performance.…”

Section: Introductionmentioning

confidence: 99%

Applying Ternion Stream DCNN for Real-Time Vehicle Re-Identification and Tracking across Multiple Non-Overlapping Cameras

Kalake

Wan

Dong

2022

Sensors

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The increase in security threats and a huge demand for smart transportation applications for vehicle identification and tracking with multiple non-overlapping cameras have gained a lot of attention. Moreover, extracting meaningful and semantic vehicle information has become an adventurous task, with frameworks deployed on different domains to scan features independently. Furthermore, approach identification and tracking processes have largely relied on one or two vehicle characteristics. They have managed to achieve a high detection quality rate and accuracy using Inception ResNet and pre-trained models but have had limitations on handling moving vehicle classes and were not suitable for real-time tracking. Additionally, the complexity and diverse characteristics of vehicles made the algorithms impossible to efficiently distinguish and match vehicle tracklets across non-overlapping cameras. Therefore, to disambiguate these features, we propose to implement a Ternion stream deep convolutional neural network (TSDCNN) over non-overlapping cameras and combine all key vehicle features such as shape, license plate number, and optical character recognition (OCR). Then jointly investigate the strategic analysis of visual vehicle information to find and identify vehicles in multiple non-overlapping views of algorithms. As a result, the proposed algorithm improved the recognition quality rate and recorded a remarkable overall performance, outperforming the current online state-of-the-art paradigm by 0.28% and 1.70%, respectively, on vehicle rear view (VRV) and Veri776 datasets.

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Development of ANPR Framework for Pakistani Vehicle Number Plates Using Object Detection and OCR

Cited by 17 publications

References 25 publications

Enhanced safety implementation in 5S + 1 via object detection algorithms

Enhanced safety implementation in 5S + 1 via object detection algorithms

Vehicle number recognition by using existing general surveillance cameras

Applying Ternion Stream DCNN for Real-Time Vehicle Re-Identification and Tracking across Multiple Non-Overlapping Cameras

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