RUOD-R: A High-Fidelity Re-Annotated Benchmark for Underwater Object Detection

Document Type

Article

Publication Date

1-1-2026

Abstract

The reliability of object detection models fundamentally depends on training data quality, yet many benchmarks suffer from missing, confused, and inaccurate bounding boxes. This issue is particularly severe in the underwater domain, where visual degradations amplify annotation errors. The Real-world Underwater Object Detection (RUOD) dataset is a widely used benchmark that provides diverse real-world underwater scenes, but its annotations reflect the inherent difficulty of comprehensive annotation in such environments. To address this limitation, we present RUOD-R, a high-quality re-annotated version of RUOD developed using a rigorous protocol that combines image enhancement and quality-assured professional annotation. RUOD-R retains the original images, ensuring that performance differences stem solely from annotation quality. RUOD-R increases the total annotated instances by 3.5×, with small object annotations increasing by over 140×. Our comparative analysis reveals that many valid object instances, particularly small and visually degraded targets, were not annotated in the original dataset, and that matched boxes exhibit localization inaccuracy (mean IoU of 0.83 with RUOD-R).We evaluate detection performance using Faster R-CNN, RetinaNet, YOLOv11, and DynYOLO, spanning general-purpose and underwater-domain-specific architectures, on both datasets. Results show that RUOD-R is a substantially more challenging benchmark due to the increased object density and the predominance of small, occluded, and visually degraded targets. A controlled evaluation on paired images where both annotation sets contain the same objects suggests that annotation density, rather than coordinate differences, is the primary factor behind the performance gap. A flip-rate analysis reveals that up to 38% of detections counted as false positives on the original dataset are correct detections of valid objects. RUOD-R thus provides a more reliable benchmark for advancing underwater object detection. The new annotations and related metadata can be found at https://github.com/RSSL-MTU/RUOD-R.

Publication Title

IEEE Access

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