Comparison of YOLOv8n and YOLOv8s Model Performance in Object Detection Using Evaluation Metrics and Confidence Scores

Abstract

Deep learning-based object detection has developed rapidly and is widely used in various computer vision applications. One of the most widely used methods is the You Only Look Once (YOLO) algorithm, which is capable of real-time object detection with a high degree of accuracy. This study aims to analyze the performance comparison of two variants of the YOLOv8 model, namely YOLOv8n and YOLOv8s, in detecting objects using evaluation metrics and confidence scores. The dataset used consists of 5000 images, which are divided into training data (70%), validation data (20%), and testing data (10%). Model performance evaluation is carried out using several object detection metrics, namely precision, recall, and mean Average Precision (mAP), as well as additional analysis in the form of computation time and confidence scores to assess the stability of the model's predictions. The results show that the YOLOv8n model achieved a precision value of 0.9313, while the YOLOv8s model achieved a recall value of 0.8415 and a mean Average Precision (mAP0.5) of 0.9055, which is slightly higher than YOLOv8n with a mAP0.5 value of 0.9009. In terms of computational efficiency, YOLOv8n has a faster training time of around 2670 seconds, compared to YOLOv8s which takes around 4477 seconds. In addition, the YOLOv8s model shows a higher confidence score, which indicates a better level of prediction confidence in detecting objects. Based on these results, it can be concluded that YOLOv8n is superior in computational efficiency, while YOLOv8s provides more stable and accurate detection performance. The results of this study are expected to serve as a reference in selecting the optimal object detection model for various computer vision-based applications