LW-UAV–YOLOv10: A lightweight model for small UAV detection on infrared data based on YOLOv10
Bibliographic record
Abstract
Advancements in unmanned aerial vehicle (UAV) technology have driven their widespread use in both civil and military sectors. Among various UAV types, small UAVs pose significant threats to global security, necessitating effective detection solutions. Real-time detection of small UAVs, especially under challenging conditions, remains a critical issue in computer vision. This study introduces LW-UAV–YOLOv10, an enhanced YOLOv10-based detection model optimized for small UAV detection using infrared data in mountainous terrain. Architectural improvements in the Backbone and Head modules enhance detection accuracy while maintaining a lightweight structure. Experimental results show that LW-UAV–YOLOv10 surpasses existing YOLO models in accuracy, speed, and suitability for real-time applications, offering a promising solution for UAV detection in complex environments. • Improved YOLOv10 model for detecting small UAVs on infrared data, bringing high efficiency. • Achieved outstanding accuracy in detecting small UAV targets in mountainous terrain conditions. • Provided real-time detection of small UAVs with fast inference speed and reliable results. • Provided effective solutions to address security challenges caused by small UAVs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".