KLASIFIKASI JENIS KENDARAAN PADA JALAN RAYA MENGGUNAKAN YOLOV7 - Bayu Aditya Pratama; Sayuti Rahman; Arnes Sembiring (Program Studi Informatika Universitas Teknologi Sumbawa, 2023)
This research aims to develop a classification system capable of identifying types of vehicles on the highway using YOLOv7 (You Only Look Once version 7), a deep learning-based object detection model that can be used for real-time object detection. With the rapid growth of traffic conditions, monitoring and managing traffic become increasingly important to reduce congestion and improve road safety. The research involves collecting image data and labeling the types of vehicles found on the highway. Subsequently, training the YOLOv7 model using the obtained dataset to classify various types of vehicles such as cars, motorcycles, trucks, and buses. The results of this study indicate that YOLOv7 can be efficiently used to classify types of vehicles on the highway with a fairly good level of accuracy, reaching a maximum of 86% for video and 91% for image detection.