Statistical Rigor in YOLO Model Evaluation: Bootstrap Confidence Intervals and Failure Mode Analysis
Abstract
The reliance on single-point metric estimates (such as a solitary mAP score) to benchmark object detection models frequently masks underlying sample variances, leading to overconfident deployment decisions. This paper presents an automated post-training validation pipeline for YOLO models designed to enforce statistical rigor in edge and production systems. First, we implement a Non-parametric Bootstrap resampling protocol (B=1000 iterations) to compute 95% Confidence Intervals (CIs) for mAP@50 and evaluate statistical significance via permutation testing. Second, we introduce an Outlier Failure Analysis module that categorizes edge-case prediction errors into False Positives, Missed Detections, Box Regression anomalies, and Class Confusion. Finally, an empirical ablation across 500 simulated A/B trials demonstrates that single-point evaluations suffer from an alarming 52.0% false positive rate under null conditions, whereas our 95% CI gating bounds Type I errors to 5.2%---fully consistent with nominal alpha.