Optimizing Real-Time Trichiasis Object Detection: A Comparative Analysis of YOLOv5 and YOLOv8 Performance Metrics

This study evaluates and compares the performance of YOLOv5 and YOLOv8 in trichiasis object detection tasks, focusing on key metrics such as mean Average Precision (mAP), specificity, recall, F1-score, and Frames Per Second (FPS). Utilizing a PC equipped with a Core i5-10500H CPU and an input size of 640*640, the results reveal YOLOv8's notable superiority over YOLOv5. YOLOv8 exhibits substantial improvements in mAP (31.8%), specificity (37.2%), recall (36.3%), and F1-score (34.4%), signifying its enhanced accuracy in identifying and classifying relevant instances. Remarkably, YOLOv8 achieves this heightened performance while maintaining competitive processing speed, as indicated by a slightly lower but comparable FPS rate. Visual samples of object detection further illustrate the algorithmic disparities, visually validating YOLOv8's proficiency. This research underscores YOLOv8's technical advancements, positioning it as a compelling choice for real-time object detection applications where precision and efficiency are paramount.