Easwari Engineering College

The published split of the public road-defect dataset BharatPotHole overlaps between training and test: 38.8% of test images have a near-duplicate in training and 99.4% come from videos also used for training. After a deduplicated, video-disjoint re-split, the same YOLO26n detector reaches AP50 0.619 on the published test set and 0.467 on its near-duplicate-free subset, a paired difference of 0.152 (95% CI 0.090–0.234). In a controlled contamination test, replacing unseen test images with copies of training frames raised a video-disjoint model's AP50 from 0.133 to 0.494, while copies of unseen frames left it near 0.15.

The published split of the public road-defect dataset BharatPotHole overlaps between training and test: 38.8% of test images have a near-duplicate in training and 99.4% come from videos also used for training. After a deduplicated, video-disjoint re-split, the same YOLO26n detector reaches AP50 0.619 on the published test set and 0.467 on its near-duplicate-free subset, a paired difference of 0.152 (95% CI 0.090–0.234). In a controlled contamination test, replacing unseen test images with copies of training frames raised a video-disjoint model's AP50 from 0.133 to 0.494, while copies of unseen frames left it near 0.15.