Papers for

underwater monitoring operators

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Underwater object detection improved by restoring degraded images after transmission

TERA: Task-Evidence Restoration for Underwater Object Detection under Bandwidth-Constrained Transmission

Abstract: Underwater object detectors are typically evaluated on pre-transmission images, overlooking additional degradation from bandwidth-constrained downscaling and compression. We introduce AquaTX, a controlled benchmark built from real underwater detection datasets with seen and held-out codecs. We also propose TERA, a metadata-blind receiver-side framework combining task-guided pixel restoration and dual-state feature correction while keeping the detector frozen. Under seen compound degradation, TERA improves mean average precision (mAP50:95, averaged over IoU thresholds from 0.50 to 0.95) from 37.7 to 50.0 on DUO-TX and from 31.5 to 41.3 on RUOD-TX relative to the frozen detector without restoration. TERA also preserves clean-input performance and remains competitive with detector fine-tuning under the held-out AVIF codec. These findings support transmission-aware evaluation and task-guided restoration for underwater object detection. Code is available at https://github.com/hijinkim/TERA.

Mon 2 NovComputer Vision and Pattern Recognition

The gist

Detecting objects underwater is harder after images are shrunk and compressed to save data bandwidth when sending them. The authors created a dataset called AquaTX to test this problem with different compression methods. They propose TERA, a way to fix images and their features on the receiving end without changing the existing detector. Their method improves detection accuracy considerably on tested datasets and keeps performance high even with new compression types. This work shows it helps to think about how images are sent when building underwater detection systems.

Open → 2611.02419v1