Deteksi Kecepatan Kendaraan Berjalan di Jalan Menggunakan OpenCV

Andrew, Andrew; Buliali, Joko Lianto; Wijaya, Arya Yudhi

doi:10.12962/j23373539.v6i2.23489

Cited by 7 publications

(7 citation statements)

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“…Penelitian "A Study Of Lane Detection Techniques And Lane Departure System" [10], penelitian "Adaptive Image Edge Detection Algorithm Based on Canny Operator" [11]. Penelitian "Deteksi Jenis Kendaraan di Jalan Menggunakan OpenCV" [12]. Penelitian " Implementasi Metode Canny Dan Sobel Untuk Mendeteksi Tepi Citra" [13].…”

Section: Pendahuluanmentioning

confidence: 99%

Implementasi Canny Edge Detection Pada Aplikasi Pendeteksi Jalur Lalu Lintas

Salkiawati

Alexander

Lubis

2021

mib

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Based on the traffic accident report, it was found that there were 41,771 (Forty-one thousand seven hundred and seventy-one) incidents caused by disorderly drivers. (POLRI, 2018). One of these disorders is by driving a motorized vehicle outside the traffic lane. In this study, researchers developed computer vision using sensor methods and image processing. The stages in computer vision are the image acquisition process, the image segmentation process, and the image understanding process. This study aims to develop an application using computer vision to warn drivers of disorderly traffic or to increase the alertness of motorized vehicle drivers by detecting the condition of the driver's path. It is hoped that this research will provide a sense of security for motorized vehicle drivers, as well as provide applications that are expected to increase driver awareness to avoid traffic accidents

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Section: Pendahuluanmentioning

confidence: 99%

Implementasi Canny Edge Detection Pada Aplikasi Pendeteksi Jalur Lalu Lintas

Salkiawati

Alexander

Lubis

2021

mib

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“…Kecepatan drone sama halnya kecepatan kendaraan yang berjalan didarat. Kecepatan sebuah kendaraan dapat diukur dan dipantau menggunakan OpenCV [5], real time dengan android [6], instant messaging di android [7], pengolahan video [8], sensor magnetic [9], motion vector [10] dan dapat juga menggunakan perpaduan antara teknik rektifikasi citra dan tapis kalman [11].…”

Section: Latar Belakang Masalahunclassified

Sending Drone Flying Speed Data Using Android-Based Client Server Method

Putra

Kusumaningrum

2020

Compiler

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- Drones as Unmanned Aerial Vehicle that are controlled using remote control are experiencing better development. The speed of the drone that cannot be accessed on the remote control can be determined through a client-server based application. The client side application, named Mydrone, is placed on the drone while on the server side, named Admin Drone, on earth. Mydrone and Admin Drone are applications made in this research based on andorid. This application on the client and server is used to monitor the speed of flying drones by using the Google Play Service library. Data sent from the client to the server in realtime by using Google Firebase. From the tests carried out it can be seen the speed of the drone when flying horisontal ly, while when flying vertically the speed is not detected. Battery capacity will also decrease when the speed of the drone gets faster, this is seen in the data when the speed is 8,741 km / h, the electricity demand taken from the battery is 31%.

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“…Di sisi lain, pengenalan objek dapat dilakukan dengan metode Haar cascade classifier yang merupakan pemodifikasian sistem face detection dalam [10]. Metode ini mengenali objek berdasarkan nilai sederhana dari fitur, tetapi bukan merupakan nilai piksel dari citra objek tersebut dan memiliki kelebihan, yaitu komputasinya sangat cepat karena hanya bergantung pada jumlah piksel dalam persegi bukan setiap nilai piksel dari sebuah citra [11]. Metode ini merupakan metode learning yang efektif dalam pendeteksian objek, yang menggabungkan beberapa konsep yaitu Haar features, integral image, AdaBoost learning, dan cascade classifier menjadi sebuah metode utama untuk mendeteksi objek [12], [13].…”

Section: Pendahuluanunclassified

“…Dalam proses kalsifikasi dan deteksi kendaraan, penggunaaan metode Haar cascade classifier dalam sistem ini memiliki kelebihan dibandingkan dengan metode-metode deteksi yang diterapkan dalam penelitian sebelumnya [6]- [8]. Haar cascade classifier mampu memberikan hasil yang baik dalam penerapannya untuk klasifikasi dan deteksi kendaraan pada jalur lalu lintas dengan akurasi 97,91% dan dapat bekerja secara baik secara real time yang didukung dengan intensitas cahaya yang baik [9]- [11].…”

Section: Hasil Dan Pembahasanunclassified

Sistem Penegakan Speed Bump Berdasarkan Kecepatan Kendaraan yang Diklasifikasikan Haar Cascade Classifier

Zulfikri

Yudaningtyas

Rahmadwati

2019

Jurnal Teknologi dan Sistem Komputer

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Driving at high speed is among the frequent causes of accidents. In this research, a warning system was developed to warn drivers when their speed beyond the safety limit. Haar cascade classifier was proposed for the detection system which comprises Haar features, integral image, AdaBoost learning, and cascade classifier. The system was implemented using Python OpenCV library and evaluated on road traffic video collected in one way traffic. As a result, the proposed method yields 97.92% of car detection accuracy in daylight and MSE of 2.88 in speed measurement.

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Deteksi Kecepatan Kendaraan Berjalan di Jalan Menggunakan OpenCV

Cited by 7 publications

References 0 publications

Implementasi Canny Edge Detection Pada Aplikasi Pendeteksi Jalur Lalu Lintas

Implementasi Canny Edge Detection Pada Aplikasi Pendeteksi Jalur Lalu Lintas

Sending Drone Flying Speed Data Using Android-Based Client Server Method

Sistem Penegakan Speed Bump Berdasarkan Kecepatan Kendaraan yang Diklasifikasikan Haar Cascade Classifier

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