An Adaptive Fast-RCNN Method for Fish Monitoring: From an Artificial Environment to the Ocean
Bibliographic record
Abstract
Due to the complexity of the marine ecosystem and the limited visibility offered by the underwater medium, the exploration of underwater environments presents numerous challenges. Camera data can provide valuable information about the underwater environment, but it’s often difficult to interpret it accurately. Nowadays, robotics and artificial intelligence advancements are now opening up new opportunities for improving underwater exploration capabilities. The aim of this paper is to develop a system that can identify and follow different elements in the submarine environment with accuracy. The proposed system will establish connections between features that have been extracted from real underwater scenes. In the proposed method, a novel visualization system is designed to enhance the interpretation of submarine environment in order to improve the decision-making capabilities of underwater vessels and autonomous robots. To extract fish characteristics and identify different fish species, an adaptive fast RCNN algorithm will be defined. On the other hand, a Kalman filter will be employed to extract the trajectory of each detected fish. In addition, fish pose in three-dimensional space will also be retrieved. The proposed system was tested using a sophisticated underwater dataset. The experimental outcomes show good progress compared to the most recent state-of-the-art methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".