Underwater fish detection in sonar image based on an improved Faster RCNN
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
For the efficient detection of underwater fish, this paper proposes a target detection algorithm based on the improved Faster region-based convolutional neural network (iFaster RCNN). On one hand, the proposed algorithm combines feature pyramid network (FPN) with the original Faster RCNN for solving the multi-scale problem in target detection. On the other hand, in order to further enhance the detection accuracy and increase detection speed, Distance-Intersection-over-Union (DIoU) is used to replace Intersection-over-Union (IoU). Experimental results show that, with FPN and DIoU, iFaster RCNN has higher detection accuracy for underwater fish. For comparison purposes, VGG16, MobileNetV2, and ResNet50 netwoks are used as the backbone feature extraction networks of iFaster RCNN. Comparative results prove that ResNet50 performs better than the other two netwoks.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it