LSCNet: A Lightweight Shallow Feature Cascade Network for Small Object Detection in UAV Imagery
Bibliographic record
Abstract
Unmanned Aerial Vehicles have become essential mobile sensing nodes in Internet of Things ecosystems, with applications ranging from disaster monitoring to traffic surveillance. However, wireless bandwidth is severely strained when sending enormous amounts of high-resolution aerial video to ground stations. To address these communication limitations, the current research paradigm is shifting toward UAV-assisted edge computing, where visual data is processed locally to extract semantic information for transmitting results to the ground or making autonomous decisions. Although deep detection is the dominant trend in general object detection, the heavy computational burden of these deep detection methods struggles to meet the stringent efficiency requirements of airborne edge platforms. Consequently, although recently proposed single-stage models like YOLOv10 can quickly detect objects in natural images, their over-dependence on deep features for computation results in wasted computational resources, as shallow information is crucial for small object detection in aerial scenes. In this paper, we propose LSCNet (Lightweight Shallow Feature Cascade Network), a novel lightweight architecture designed for UAV edge computing to handle aerial object detection tasks. Our lightweight Cascade Network focuses on feature extraction and shallow feature enhancement. LSCNet achieves 44.6% mAP50 on VisDrone2019 and 36.1% mAP50 on UAVDT, while decreasing parameters by 33% to 1.48 M. These results not only show how effective LSCNet is for real-time object detection but also provide a foundation for future developments in semantic communication within aerial networks.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".