Implementing ALPR for detection of traffic violations: a step towards sustainability

Agarwal, Parul; Chopra, Kritika; Kashif, Muhammad; Kumari, Vinita

doi:10.1016/j.procs.2018.05.085

Cited by 22 publications

(15 citation statements)

References 19 publications

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“…Kemudian penelitian terkait tentang penerapan deteksi pelat nomor kendaraan untuk pelanggaran lalu lintas [10] Kamera digunakan untuk mengambil gambar dari pelat nomor kendaraan kemudian gambar ini disempurnakan menggunakan berbagai metode seperti erosi, penipisan, konvolusi dan terakhir menggunakan Teknik OCR (Pengenalan Karakter Optik). Dari 100 gambar plat nomor yang diuji coba 93 gambar berhasil diidentifikasi dengan benar.…”

Section: Tinjauan Pustakaunclassified

Deteksi Plat Nomor Kendaraan Otomatis Dengan Convolutional Neural Network Dan Ocr Pada Tempat Parkir Itb Ahmad Dahlan Lamongan

Galahartlambang,

Khotiah,

Zahruddin Fanani

et al. 2023

misi

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Pengenalan plat nomor kendaraan secara otomatis adalah konsep penting saat ini karena pesatnya pertumbuhan mobil, sepeda motor, dan kendaraan lainnya. Sistem pengenalan plat nomor otomatis ini menggunakan teknologi pemrosesan gambar untuk identifikasi kendaraan. Sistem ini dapat digunakan di daerah padat lalu lintas untuk memudahkan identifikasi kendaraan yang melanggar peraturan lalu lintas dan nama pemilik, alamat dan informasi lainnya dapat diambil dengan menggunakan sistem ini. Sistem cerdas dapat memainkan peran penting dalam pendeteksian pelat nomor kendaraan. Pada penelitian ini dikembangkan sebuah sistem untuk mendeteksi dan mengenali pelat nomor kendaraan menggunakan convolutional neural network (CNN), sebuah teknik deep learning, yang bertujuan untuk menyajikan sebuah sistem cerdas untuk pengenalan plat nomor kendaraan secara otomatis berbasis teknik pengolahan citra digital. Sistem ini terdiri dari dua bagian yaitu deteksi plat nomor dan pengenalan plat nomor. Pada bagian deteksi, citra kendaraan ditangkap melalui kamera digital. Kemudian sistem membagi wilayah pelat nomor dari bingkai gambar. Setelah mengekstrak wilayah pelat nomor, metode resolusi super diterapkan untuk mengubah gambar dari beresolusi rendah menjadi gambar beresolusi tinggi. Teknik resolusi super digunakan dengan convolutional layer CNN untuk merekonstruksi kualitas piksel dari citra masukan. Setiap karakter plat nomor disegmentasi berdasarkan area pengamatan (region of interest). Pada bagian pengenalan, fitur diekstraksi dan diklasifikasikan menggunakan model CNN. Hasil penelitian menunjukkan deteksi area plat nomor menunjukkan akurasi 98 % sedangkan pembacaan karakter nomor kendaraan menunjukkan akurasi 88 %.

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Section: Tinjauan Pustakaunclassified

Deteksi Plat Nomor Kendaraan Otomatis Dengan Convolutional Neural Network Dan Ocr Pada Tempat Parkir Itb Ahmad Dahlan Lamongan

Galahartlambang,

Khotiah,

Zahruddin Fanani

et al. 2023

misi

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“…In [23], the morphological process with convolution scores 93% in localizing and recognizing Indian plate samples. Top-hat Transform is a morphological process proposed for LP localization [24], [25], which achieved an accuracy of 96%.…”

Section: Morphological Processesmentioning

confidence: 99%

License Plate Recognition Techniques: Comparative Study

Abdullah,

Mohammed Al-Nawah,

Osman

et al. 2021

MJCS

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Vehicle License Plate Recognition (LPR) has become a crucial system for various applications such as security monitoring, parking access, law enforcement and so on. LPR is employed for the identification of vehicles using their license plate. Recently, LPR technology has evolved progressively where vast improvement had been made towards the development of the image recognition’s quality and speed, as well as its state of the art methods. Although several research studies managed to resolve most of the issues that arise in LPR systems, more studies need to be conducted to improve the performance of LPR. This paper aims to provide a comprehensive analysis and comparison of different methods used in LPR. It summarizes each of the methods in terms of their accuracy, performance, strengths and weaknesses. Based on the recognition techniques used, LPR is then characterized into two categories, namely Traditional Computer Vision and Deep Learning Techniques.

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“…e system was however an effective system. e authors in [13] implemented an ALPR for detecting traffic violators for promoting sustainability in modern transport systems. is system made use of a camera to capture the plate number of vehicles.…”

Section: Related Workmentioning

confidence: 99%

Development of Smart Plate Number Recognition System for Fast Cars with Web Application

Shobayo

Olajube

Ohere

et al. 2020

Applied Computational Intelligence and Soft Computing

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Traffic law violation has been recognized as a major cause for road accidents in most parts of the world with majority occurring in developing countries. Even with the presence of rules and regulations stipulated against this, violators are still on the increase. This is due to the fact that the rules are not properly enforced by appropriate authorities in those parts of the world. Therefore, a system needs to be designed to assist law enforcement agencies to impose these rules to improve road safety and reduce road accidents. This work uses a Vehicle Plate Number Recognition (VNPR) system which is a real-time embedded system to automatically recognize license plate numbers. It provides an alternative means to VPNR using an open-source library known as openCV. The main aim of the system is to use image processing to identify vehicles violating traffic by their plate numbers. It consists of an IR sensor for detecting the vehicle. During testing, a minimum time was set for the sensor to detect the object which was recorded by the microprocessor. Once it was less than the set time, the camera was triggered to capture the plate number and store the image on the Raspberry Pi. The image captured is processed by the Raspberry Pi to extract the numbers on the image. The numbers on the capture imaged were viewed on a web page via an IP address. The system if implemented can be used to improve road safety and control traffic of emerging smart cities. It will also be used to apply appropriate sanctions for traffic law violators.

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Implementing ALPR for detection of traffic violations: a step towards sustainability

Cited by 22 publications

References 19 publications

Deteksi Plat Nomor Kendaraan Otomatis Dengan Convolutional Neural Network Dan Ocr Pada Tempat Parkir Itb Ahmad Dahlan Lamongan

Deteksi Plat Nomor Kendaraan Otomatis Dengan Convolutional Neural Network Dan Ocr Pada Tempat Parkir Itb Ahmad Dahlan Lamongan

License Plate Recognition Techniques: Comparative Study

Development of Smart Plate Number Recognition System for Fast Cars with Web Application

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