Lightweight and Dynamic Content-Augmented Object Detection for UAVs
Bibliographic record
Abstract
This paper presents a novel lightweight and robust object detection framework tailored for UAV-based applications. The core of the proposed method is the Dynamic Content-Augmented Feature Pyramid Network (DCA-FPN), which integrates a Global Content Extraction Module (GCEM), an Adaptive Branching Network (ABN), and a Linear Transformer (LT) to enhance multi-scale feature representation and contextual understanding. These components collectively improve detection performance for small and occluded objects while addressing the misalignment issues inherent in traditional feature pyramids. Built on a MobileNet backbone with depthwise separable convolutions, the framework offers low computational complexity and real-time readiness for edge devices. Experimental results on the Vis-Drone dataset show a state-of-the-art mean Average Precision (mAP) of 42.50%, while additional evaluations on the MS COCO benchmark confirm competitive performance across diverse object scales. Furthermore, testing on the GDIT Aerial Airport dataset demonstrates the model’s applicability in infrastructure monitoring tasks, particularly in detecting airplanes across varied sizes and conditions. These results highlight the robustness, efficiency, and deployment potential of the proposed framework in real-world, resource-constrained UAV scenarios.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".