Remote Sensing for Mining Detection: Comparative Analysis of Deep Learning Models
Bibliographic record
Abstract
Remote sensing combined with Deep Learning (DL) is gaining popularity for autonomous mining detection. However, it is observed that many DL models were assessed on custom datasets, which are not publicly accessible, with comparisons restricted to baseline models such as YOLOv5 and Faster R-CNN instead of other existing methods within the mining detection field. To address this gap, this paper provides a comparative analysis of six algorithms, including a baseline model (YOLOv5) and five algorithms derived from three recently proposed DL models consisting of cutting-edge algorithms such as convolutional neural networks, multiscale feature extraction, and attention modules. All six algorithms were executed and evaluated on a single, publicly available mining detection dataset, CUG_MISDataset. The results show that the MFTAYOLO algorithm demonstrated superior performance, achieving the highest mean average precision (mAP@50) with a 0.7 % improvement compared to other algorithms, alongside an approximate 17 % reduction in parameter count compared to the baseline model, YOLOv5.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".