Papers › Underwater Soft Coral Detection: SCoralNet for Accurate and Efficient Annotation.
Underwater Soft Coral Detection: SCoralNet for Accurate and Efficient Annotation.
Zhaoxuan Lu; Xingang Xie; Xiaolong Zhu
SCoralNet (based on Faster R-CNN) is a new underwater coral detection framework that has been proposed to automatically localize and identify distinct coral species in images, allowing for fast and detailed annotation. Monitoring the coverage and abundance of underwater corals typically involves the annotation and processing of large amounts of underwater coral images. However, manually annotating a large number of images is time-consuming and labor-intensive, and CNN classifiers only provide simple classification annotations without capturing the images' finer details. SCoralNet's detection performance is improved by incorporating dilated convolutions into the backbone network. To successfully capture multi-scale and multi-level information from coral targets, a neck network called NASFPN is placed between the backbone and the detecting head. Seesaw Loss is used to reduce the impact of the dataset's long-tailed distribution on SCoralNet's classifier accuracy. CIoU loss is used to optimize the bounding box regression method. During inference, Soft-NMS is applied to suppress redundant coral detection boxes. To assess SCoralNet's effectiveness, a dataset called Coral-soft was developed using real-world photos of common soft coral species from the Sanya region of China. SCoralNet outperformed the original Faster R-CNN model on the Coral-soft dataset, with a 45.68% gain in mean average precision (mAP) and a 59.2% increase in mAP75. Furthermore, SCoralNet outperformed some advanced models in terms of overall performance.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 2D Object Detection | SCoralDet Dataset | SCoralNet | mAP | 53.9 | #3 of 3 | Archive leaderboard | report |
| 2D Object Detection | SCoralDet Dataset | SCoralNet | mAP50 | - | #3 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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