Storage tank detection in remote sensing images based on circular bounding boxes and large selective kernel
Bibliographic record
Abstract
Accurate storage tank detection in remote sensing images is vital for monitoring methane emissions, a potent greenhouse gas, from the oil and gas industry. Existing methods, such as traditional geometric and spectral feature-based approaches, suffer from high false detection rates due to background variations and imaging conditions, while deep learning models like YOLO series and EfficientDet struggle with small objects, multi-scale features, background interference, and regression sensitivity, leading to missed detections and false positives. This study introduces a novel method integrating circular bounding boxes and a Large Selective Kernel (LSK) to address these gaps. Circular bounding boxes, aligned with storage tanks' typical circular shape, stabilize Intersection over Union (IoU) for small objects, while LSK dynamically adjusts the receptive field to leverage contextual information effectively. Implemented on a YOLO-v10 framework and evaluated on a comprehensive dataset comprising DIOR, NWPUU_RESISC45, NWPU VHR-10, TGRS-HRRSD, and a self-built dataset (totaling 3568 images and 46075 storage tanks), our approach achieved a precision of 0.911, recall of 0.902, and mean Average Precision (mAP@0.5) of 0.931. These results represent improvements of 2.0% in precision, 2.7% in recall, and 1.8% in mAP@0.5 over the state-of-the-art YOLO-v10 baseline, offering a robust tool for pinpointing methane emission sources and supporting environmental sustainability efforts in the oil and gas sector.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".