Applying YOLOv12 with physics-flavored augmentations yields high reported mAP on four underwater detection benchmarks, but the claims are weakened by missing code, variance, and inconsistent speed numbers.
Underwater object detection method based on improved faster rcnn,
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Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation
Applying YOLOv12 with physics-flavored augmentations yields high reported mAP on four underwater detection benchmarks, but the claims are weakened by missing code, variance, and inconsistent speed numbers.