计算机科学
目标检测
实时计算
传输(电信)
人工智能
延迟(音频)
对象(语法)
计算机视觉
能见度
视频跟踪
模式识别(心理学)
电信
物理
光学
作者
Vitradisa Pratama,Agus Sukoco,Pebi Pebriadi,Dyah Indriana Kusumastuti,Vip Paramarta,Ary Setijadi Prihatmanto
标识
DOI:10.1109/tssa59948.2023.10366964
摘要
In the realm of autonomous vehicles, object detection holds a pivotal role in enabling accurate perception and safe navigation within complex environments. This study incorporates these domains by proposing a vehicle detection system employing YOLO v8 within the popular game Forza Horizon 4. YOLO v8’s real-time, accurate object detection prowess aligns seamlessly with the game’s dynamics, enriching the user experience through improved vehicle tracking. The research encompasses YOLO v8 model training on an expansive vehicle dataset, adaptation for Forza Horizon 4, and rigorous evaluation, comparing accuracy, speed, and efficiency against existing methods. The methodology entails dataset preparation, YOLO v8 configuration, object detection, result evaluation, optional fine-tuning, and iterative testing and optimization. By scrutinizing two data transmission protocols, RTMP and HTTPS, the research accentuates RTMP’s superiority in real-time image data transmission for object detection, surpassing HTTPS in processing speed. While RTMP excels in low-latency scenarios, HTTPS augments security through encrypted data transmission. The findings prove RTMP’s speed in image data transmission for real-time object detection, whereas HTTPS’s secure communication advantages.
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