Object Tracking Menggunakan Algoritma You Only Look Once (YOLO)v8 untuk Menghitung Kendaraan

Nurhaliza Juliyani Hayati, Dayan Singasatia, Muhamad Rafi Muttaqin
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Abstract

Vehicles are a means of transportation that have existed from ancient times until now, many people use vehicles such as cars and motorbikes. Enumeration of types and numbers of vehicles is carried out to collect traffic data information. In obtaining data parameters for the number of vehicles, manual calculations are usually prone to errors and take a lot of time and energy. The application of Artificial Intelligence such as object detection is a field of computer vision. In intelligent transportation systems, traffic data is the key to conducting research and designing a system. To overcome the problem, researchers carried out object tracking using the You Only Look Once (YOLO) v8 algorithm to detect the type and count the number of vehicles. The methodology applied is the AI Project Cycle stages which use problem scoping, data acquisition, data exploration, modeling, and confusion matrix evaluation. The results of the confusion matrix evaluation obtained an accuracy level of 89%, precision of 89%, recall of 90% and a weighted comparison of precision and recall obtained an F1-Score value of 89%. Thus, the You Only Look Once (YOLO) v8 algorithm is accurate enough to detect object tracking to calculate vehicles.
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目标跟踪算法You Only Look Once (YOLO)v8
车辆是一种从古至今一直存在的交通工具,许多人使用汽车和摩托车等交通工具。通过列举车辆种类和数量来收集交通数据信息。在获取车辆数量的数据参数时,人工计算往往容易出错,耗费大量的时间和精力。物体检测等人工智能的应用是计算机视觉的一个领域。在智能交通系统中,交通数据是进行系统研究和设计的关键。为了克服这个问题,研究人员使用You Only Look Once (YOLO) v8算法进行了目标跟踪,以检测车辆的类型并计算车辆的数量。应用的方法是人工智能项目周期阶段,使用问题范围界定、数据采集、数据探索、建模和混淆矩阵评估。混淆矩阵评价结果的准确率为89%,准确率为89%,召回率为90%,准确率和召回率加权比较的F1-Score值为89%。因此,You Only Look Once (YOLO) v8算法足够精确,可以检测物体跟踪以计算车辆。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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