7 research outputs found

    PENGGUNAAN DEEP LEARNING UNTUK KLASIFIKASI KENDARAAN BERBASIS CITRA DALAM KAWASAN TERTIB LALU LINTAS DI KABUPATEN SUMEDANG

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    Penelitian ini dilatar belakangi oleh banyaknya pelanggar kawasan tertib lalu lintas. Dalam kawasan tertib lalu lintas di Kab. Sumedang, ada beberapa jenis kendaraan yang tidak diperbolehkan untuk memasuki kawasan tersebut. Adanya ATCS belum dimanfaatkan secara maksimal oleh Dinas Perhubungan terutama pada bidang deep learning. Penelitian ini bertujuan untuk melakukan penerapan program pengolahan citra untuk mendeteksi kendaraan menggunakan algoritma YOLOv5 melalui rekaman CCTV lalu lintas; melakukan pengujian terhadap hasil pengolahan citra kendaraan melalui rekaman CCTV lalu lintas; dan memberikan rekomendasi pengembangan sistem pendeteksian kendaraan pada kawasan tertib lalu lintas di Kab. Sumedang. Penelitian ini menerapkan AI Project Cycle dan algoritma YOLOv5 sebagai algoritma yang digunakan untuk mendeteksi kendaraan. Dataset didapat melalui video yang ada pada website ATCS Dinas Perhubungan Kab. Sumedang. Berdasarkan hasil penelitian diperoleh kesimpulan bahwa arsitektur yang digunakan melalui hasil dari training serta validasi dalam model YOLOv5 berhasil mendeteksi dengan akurat; serta model ini dapat mendeteksi keempat jenis kendaraan dengan cukup baik dengan mendapatkan nilai mAP di semua kelas sebesar 91,6%. -----The background of this research is based on the number of violators in the traffic order area. In the traffic order area in Sumedang Regency, several types of vehicles are not allowed to enter the area. The existence of ATCS has not been fully utilized by the Department of Transportation, especially in the field of deep learning. This study aims to implement an image processing program to detect vehicles using the YOLOv5 algorithm through traffic CCTV recordings; conduct testing on the results of vehicle image processing through CCTV traffic recordings; and provide architectural recommendations for the development of vehicle detection systems in traffic orderly areas in Kab. Sumedang. This study applies the AI Project Cycle and uses YOLOv5 as the algorithm that will be used to detect vehicles. The dataset used was obtained through a video on the ATCS website of the Department of Transportation in Kab. Sumedang. Based on the results of the study, it was concluded that the architecture used through the results of training and validation in the YOLOv5 model managed to detect accurately; and this model can detect the four types of vehicles quite well by getting an average mAP value in all classes of 91.6%

    YOLO Algorithm for Detecting People in Social Distancing System

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    Social distancing is an effort to prevent the spread of the coronavirus. Several systems for monitoring social distancing have been developed. People detection is an essential step in implementing a social distancing system. Failure to detect people causes the social distancing system to be inaccurate. Two people who communicate cannot occur violations of social distancing because one person is not detected. Therefore, we propose a precise person detection method for the social distancing system. The proposed social distancing system uses the YOLOv3 method for people detection and Euclidean Distance for measuring the distance of social distancing. YOLOv3 can detect people's objects precisely, even people who are caught small by the camera. Experiments on two outdoor video datasets result in an F1 value of more than 0.8. This proposed system can serve as a reference for future social distancing research

    YOLO Algorithm for Detecting People in Social Distancing System

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    Social distancing is an effort to prevent the spread of the coronavirus. Several systems for monitoring social distancing have been developed. People detection is an essential step in implementing a social distancing system. Failure to detect people causes the social distancing system to be inaccurate. Two people who communicate cannot occur violations of social distancing because one person is not detected. Therefore, we propose a precise person detection method for the social distancing system. The proposed social distancing system uses the YOLOv3 method for people detection and Euclidean Distance for measuring the distance of social distancing. YOLOv3 can detect people's objects precisely, even people who are caught small by the camera. Experiments on two outdoor video datasets result in an F1 value of more than 0.8. This proposed system can serve as a reference for future social distancing research

    An Approach to Counting Vehicles from Pre-Recorded Video Using Computer Algorithms

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    One of the fundamental sources of data for traffic analysis is vehicle counts, which can be conducted either by the traditional manual method or by automated means. Different agencies have guidelines for manual counting, but they are typically prepared for particular conditions. In the case of automated counting, different methods have been applied, but You Only Look Once (YOLO), a recently developed object detection model, presents new potential in automated vehicle counting. The first objective of this study was to formulate general guidelines for manual counting based on experience gained in the field. Another goal of this study was to develop a computer program for vehicle counting from pre-recorded video applying the YOLO model. The documented general guidelines provided in this project can be useful in acquiring the required standard and minimizing the cost of a manual counting project. The accuracy of the automated counting program was found to be about 90 percent for total daily counts, although most of that error was a consistent undercounting by automated counting

    Early Warning System Untuk Prediksi Tingkat Pelayanan Jalan di Jalur SSA Kota Bogor

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    Kepadatan lalu lintas yang cukup tinggi pada akhir pekan dan hari kerja dikarenakan kota Bogor merupakan kota tujuan wisata serta penunjang kegiatan di DKI Jakarta, sehingga menyebabkan over capacity. Penerapan jalur SSA dilakukan sebagai upaya untuk mengurangi tingkat kemacetan yang terjadi pada jalur tersebut. Maksud dari penelitian ini adalah untuk membuat suatu model prediksi yang dikembangkan dalam sebuah sistem aplikasi yang bisa digunakan untuk mendekteksi kemacetan terutama di ruas sistem satu arah kota Bogor. Pengumpulan data dilakukan beberapa proses diantaranya melakukan survey dan pengamatan lalu lintas di jalur SSA pada Dinas Perhubungan Kota Bogor. Prediksi kemacetan arus lalu lintas menggunakan metode ANFIS (Adaptive Neuro Fuzzy Inference System). Hasil prediksi ANFIS kemudian digunakan untuk mengukur tingkat pelayanan jalan berdasarkan karakteristik arus lalu lintas yang ditandai dalam suatu nilai rasio perbandingan antara volume kendaraan dan kapasitas jalan. Pada hasil prediksi yang sudah dilakukan diketahui jumlah kendaraan yang melewati jalus SSA mencapai 5034 dengan kapasitas jalan 6400. Sehingga status kemacetan yang terjadi berada di level C dengan nilai 0,78. Dimana tingkat Pelayanan pada nilai rasio tersebut memiliki karakteristik arus stabil tetapi pergerakan kendaraan dikendalikan oleh volume lalu lintas yang lebih tinggi dengan kecepatan sebesar 60 Km/Jam dan kepadatan lalu lintas sedang
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