One-class SVM for landmine detection and discrimination
Bibliographic record
Abstract
In this paper, we present landmine detection and discrimination method: one class support vector machine (OSVM) based on RBF kernel using one-dimensional Ground Penetrating Radar (GPR) delivered data. The GPR has been a precious tool for humanitarian demining. It scans the ground and delivers a three-dimensional matrix representing three types of data; Ascan, Bscan and Cscan. The Ascan data represents the response from a reflection signal of a pulse emitted by the GPR at a given position. The normalized Ascan data is the input data of our proposed landmine detection method. One Class SVM has been tested on the MACADAM database which is composed of 11 scenarios of target class (landmines) and 5 scenarios of outliers class (wood stick, Soda Can, pine, stone), each evaluation scenario contains six buried objects in various buried depth which varied between -70 and 100 mm. OSVM based on RBF kernel has been compared to the OSVMs based on Polynomial kernel, Linear kernel and Sigmoid kernel in term of classification accuracy. Obtained experimental results which are 89.24% as AUC and 0.959s as running time prove that one class SVM based on RBF kernel is out performs than the others classifiers in terms of landmine detection and discrimination.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".