Enhancement of Sonar Detection in Karst Caves Through Advanced Target Location and Image Fusion Algorithms
Bibliographic record
Abstract
Over extended periods, nature's forces have sculpted and solidified unique cave formations.The scientific community's increasing interest in studying these karst caves has highlighted the significance of advanced detection methods.Sonar caving, a prevalent technique, still grapples with comprehensive detection within these intricate structures.This study delves into the amalgamation of target location and image fusion algorithms to bridge this gap.Target location elucidates an object's position, dimensions, and intrinsic characteristics within a designated temporal and spatial domain.Image fusion, a leading topic within image processing, leverages the consolidation of diverse data types and formats, thereby playing a pivotal role in image enhancement, compression, and recognition.From a deep learning perspective, neural network output values were calculated, facilitating an improvement in positioning accuracy.Further, through matrix decomposition and wavelet transforms, the fusion efficacy was scrutinized, aiming to broaden the sonar detection ambit.Comparative experimentation underscored the efficacy of integrating these two algorithms in sonar-based karst cave detection.It was observed that not only did the image clarity amplify, but there was also a notable 7.93% augmentation in positioning accuracy and a surge in detection speed.Cumulatively, a 7.64% boost in the overall efficiency of sonar detection in karst caves was achieved, underscoring the imperative nature of these technological advancements for cave surveillance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".