Adaptive landmine detection and localization system based on incremental one-class classification
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
Clearing large civilian areas of antipersonnel landmines is a very severe problem that should be solved efficiently. Although many methods have been developed for landmine detection and discrimination using ground penetrating radar data, the problem has not yet been properly solved, especially, as landmine and innocuous object classes are imbalanced. One-class classification is a competitive method for landmine detection as data are unbalanced, but it separates the target from outliers along the target class large variance directions, which results in higher error. As a solution, covariance-guided one-class support vector machine (COSVM) emphasizes low-variance projectional directions of the training data, which results in high accuracy and error minimization. However, in the case of a large-scale dataset, COSVM requires a large memory and enormous amount of training time. Moreover, it is inflexible with dynamic data. For these reasons, we investigate the effectiveness of incremental covariance-guided one-class support vector machine (ICOSVM) to build an adaptive landmine detection and localization system. In fact, the ICOSVM has the advantage of incrementally projecting the data onto low-variance directions, thereby improving detection performance. Experimental results have shown clearly the superiority and efficiency of the proposed landmine detection and localization system.
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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