Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Study
Rana Poureskandar, Shiva Razzagzadeh
TL;DR
This paper presents a comprehensive training and evaluation study of YOLOv8 for object detection, focusing on improving performance through detailed analysis and optimization techniques.
Contribution
It introduces new training strategies and evaluation methods specifically tailored for YOLOv8 to enhance object detection accuracy.
Findings
YOLOv8 achieves state-of-the-art detection accuracy.
Optimized training procedures significantly improve performance.
The model performs well across diverse facial wrinkle datasets.
Abstract
This study evaluated the performance of a YOLOv8-based segmentation model for detecting and segmenting wrinkles in facial images.
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Taxonomy
TopicsFace recognition and analysis · Face and Expression Recognition · Visual Attention and Saliency Detection