Analysis of Helmet Detection on Motor Drivers to Detect Traffic Violations Using the You Only Look Once Method (Yolov4)

According to statistical data, the number of deaths due to 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 monitoring is the identification of motorbikes in traffic based on Deep Learning. In this study, the 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 helmets that comply with LLAJ or SNI (Indonesian National Standard) helmets before riding. Trending Machine Learning and Deep Learning conduct research to discover new methods and advanced architectures such as YOLO (You Only Look Once). YOLO is an object detection network architecture that is claimed to be the "fastest deep learning object detector" that prioritizes 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%