FEBRIANA, NADIA HANIFA (2023) ANALISIS DETEKSI HELM PADA PENGENDARA BERMOTOR UNTUK MENDETEKSI PELANGGARAN LALU LINTAS MENGGUNAKAN METODE YOU ONLY LOOK ONCE (YOLOv4). Undergraduate thesis, Universitas Pembangunan Nasional "Veteran" Jawa Timur.
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Abstract
According to statistical data, the number of victims who died as a result of accidents in Indonesia in 2017 was 30,568 people. Efforts are being made to reduce traffic violations, especially helmet violations. Helmets that must be worn by Indonesian motorcyclists must comply with the Indonesian National Standard (SNI), but there are still many non-SNI helmets circulating. A possible solution for coaching is to call a motorbike in traffic based on Deep Learning. In this study, detection and classification of helmets was carried out using the YO-LO (You Only Look Once) method. The SNI helmet detection system aims to make drivers more disciplined in completing their riding equipment, especially helmets with SNI, because this system requires riders to wear a helmet that complies with LLAJ or an SNI (Indonesian National Standard) helmet before riding. Trending Machine Learning and Deep Learning make research to discover new methods and advanced architectures like YOLO (You Only Look Once). YOLO is a network object detection architecture that is claimed to be the "fastest deep learning object detector" that emphasizes accuracy and speed. With YOLOv4, violations by motorbike riders can be detected in real time and whether the riders recorded on the camera are directly wearing SNI helmets, non-SNI helmets or not wearing helmets. The best accuracy for real-time motorcyclist violations with YOLOv4 is the best mAP value of 99.69%. Keywords: YOLOv4, Deep Learning, Darknet, Breach, Accident
| Item Type: | Thesis (Undergraduate) | ||||||||||||
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| Subjects: | T Technology > T Technology (General) | ||||||||||||
| Divisions: | Faculty of Computer Science > Departemen of Informatics | ||||||||||||
| Depositing User: | Nadia Hanifa Febriana | ||||||||||||
| Date Deposited: | 24 Jul 2023 07:06 | ||||||||||||
| Last Modified: | 24 Jul 2023 07:06 | ||||||||||||
| URI: | http://repository.upnjatim.ac.id/id/eprint/16143 |
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