An Efficient Car Parking Management System using raspberry-pi

Aravindh, G.; Arunkumar, M.

doi:10.1109/iciss49785.2020.9316049

“…Based on previous research, it was discovered that the typical approach to the problem consisted of two modules: (a) an ANPR module using a CNN model or object detection models such as YOLO and its variants, and (b) an OCR module using image filtering techniques and then passing through a CNN Network. Several Works [4] [5] [6] have used Raspberry-Pi to achieve these ideas but have used comparable approaches in the ANPR and OCR modules.Table 1 given below lists some of the prior works that used these methodologies. YOLOv4 [12] Custom CNN Automatic License Plate Recognition for Parking System using CNNs [13] YOLO [8] ResNet [14] A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector [15] YOLO [8] Custom CNN An automated vehicle parking monitoring and management system using ANPR cameras [16] Custom CNN Custom CNN…”

Section: Previous Workmentioning

confidence: 99%

Chaurah: A Smart Raspberry Pi based Parking System

Choudhaury,

Narendra,

Mishra

et al. 2024

Preprint

0

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The widespread usage of cars and other large, heavy vehicles necessitates the development of an effective parking infrastructure. Additionally, algorithms for detection and recognition of number plates are often used to identify automobiles all around the world where standardized plate sizes and fonts are enforced, making recognition a effortless task. As a result, both kinds of data can be combined to develop an intelligent parking system centered on ANPR technology. Extraction of the license plate characters from a photo is the primary objective of ANPR. Typically, this procedure is expensive. In this work, we introduce Chaurah, a low-cost Raspberry-Pi 3 based ANPR system designed especially for parking facilities. The system uses two stages of technique, the first of which is an ANPR system that uses two convolutional neural networks (CNNs). The first one uses a vehicle image to find and recognize license plates, while the second one uses optical character recognition to extract the licence plate numbers. The second step of the solution consists of a user-facing application made with Flutter and Firebase for database management in order to compare licence plate records. The app also functions as an interface for a payment system depending on the length of parking time, making it a full software embodiment of the concept.

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“…From the study off the previous works it was found that the typical approach to the problem constituted of two modules mainly (a) ANPR module using a CNN model or object detection models like YOLO and its other versions and (b) OCR module using image filtering techniques and then passing through a CNN Network. Some works [30][31] [32] have implemented these approaches using a Raspberry Pi as well but have implemented the similar approaches in the ANPR and OCR modules Our solution proposes the use of YOLOV4 [6] for the ANPR module and Keras OCR pipeline for the OCR module and also uses an Raspberry-Pi for its working making it an unique and low-cost solution for an parking system. Some of the previous works that involved these approaches are given in YOLO [6] ResNet [27] Based on the YOLO Detector, a Powerful Real-Time Automatic License Plate Recognition 28] Employing ANPR cameras, an automated system for managing and monitoring car parking [29] YOLO [6] Custom CNN Custom CNN Custom CNN…”

Section: Previous Workmentioning

confidence: 99%

Chaurah: A Smart Raspberry Pi based Parking System

Narendra¹,

Choudhury²,

Mishra³

et al. 2023

Preprint

0

View full text Add to dashboard Cite

<p>The widespread usage of cars and other large, heavy vehicles necessitates the development of an effective parking infrastructure. Additionally, algorithms for detection and recognition of number plates are often used to identify automobiles all around the world where standardized plate sizes and fonts are enforced, making recognition a effortless task. As a result, both kinds of data can be combined to develop an intelligent parking system centered on ANPR technology. Extraction of the license plate characters from a photo is the primary objective of ANPR. Typically, this procedure is expensive. In this work, we introduce Chaurah, a low-cost Raspberry-Pi 3 based ANPR system designed especially for parking facilities. The system uses two stages of technique, the first of which is an ANPR system that uses two convolutional neural networks (CNNs). The first one uses a vehicle image to find and recognize license plates, while the second one uses optical character recognition to extract the licence plate numbers. The second step of the solution consists of a user-facing application made with Flutter and Firebase for database management in order to compare licence plate records. The app also functions as an interface for a payment system depending on the length of parking time, making it a full software embodiment of the concept.</p>

show abstract

“…From the study off the previous works it was found that the typical approach to the problem constituted of two modules mainly (a) ANPR module using a CNN model or object detection models like YOLO and its other versions and (b) OCR module using image filtering techniques and then passing through a CNN Network. Some works [30][31] [32] have implemented these approaches using a Raspberry Pi as well but have implemented the similar approaches in the ANPR and OCR modules Our solution proposes the use of YOLOV4 [6] for the ANPR module and Keras OCR pipeline for the OCR module and also uses an Raspberry-Pi for its working making it an unique and low-cost solution for an parking system. Some of the previous works that involved these approaches are given in YOLO [6] ResNet [27] Based on the YOLO Detector, a Powerful Real-Time Automatic License Plate Recognition 28] Employing ANPR cameras, an automated system for managing and monitoring car parking [29] YOLO [6] Custom CNN Custom CNN Custom CNN…”

Section: Previous Workmentioning

confidence: 99%

Chaurah: A Smart Raspberry Pi based Parking System

Narendra¹,

Choudhury²,

Mishra³

et al. 2023

Preprint

0

View full text Add to dashboard Cite

<p>The widespread usage of cars and other large, heavy vehicles necessitates the development of an effective parking infrastructure. Additionally, algorithms for detection and recognition of number plates are often used to identify automobiles all around the world where standardized plate sizes and fonts are enforced, making recognition a effortless task. As a result, both kinds of data can be combined to develop an intelligent parking system centered on ANPR technology. Extraction of the license plate characters from a photo is the primary objective of ANPR. Typically, this procedure is expensive. In this work, we introduce Chaurah, a low-cost Raspberry-Pi 3 based ANPR system designed especially for parking facilities. The system uses two stages of technique, the first of which is an ANPR system that uses two convolutional neural networks (CNNs). The first one uses a vehicle image to find and recognize license plates, while the second one uses optical character recognition to extract the licence plate numbers. The second step of the solution consists of a user-facing application made with Flutter and Firebase for database management in order to compare licence plate records. The app also functions as an interface for a payment system depending on the length of parking time, making it a full software embodiment of the concept.</p>

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An Efficient Car Parking Management System using raspberry-pi

Cited by 10 publications

References 6 publications

Chaurah: A Smart Raspberry Pi based Parking System

Chaurah: A Smart Raspberry Pi based Parking System

Chaurah: A Smart Raspberry Pi based Parking System

Chaurah: A Smart Raspberry Pi based Parking System

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