PloS one
Comparative evaluation of YOLO models for retained root fragment detection
By Ma C, He L, Xia K and 2 others.
Abstract
Objective
This study aimed to develop and comparatively evaluate four YOLO nano-scale one-stage object detectors-YOLOv8n, YOLOv10n, YOLO11n, and YOLO12n-for the automated detection of retained root fragments in dental radiographic images, under identical preprocessing, training, and evaluation protocols.
Methods
A curated dataset was organized in the YOLO format and evaluated using a nested 5-fold cross-validation strategy to prevent data leakage. An ablation study was additionally conducted to validate the combined bilateral filtering and histogram equalization (BF + HE) preprocessing pipeline. Four YOLO models were fine-tuned from official pretrained weights over 200 epochs. Detection performance was assessed at both the epoch level and the decision-threshold level, reporting the unbiased mean and standard deviation across the independent test folds for precision, sensitivity, F1 score, mean average precision (mAP), and false positives per image (FP/image).