RECOD: Resource-Efficient Camouflaged Object Detection for UAV-Based Smart Cities Applications
Bibliographic record
Abstract
This paper introduces a novel method for detecting camouflaged objects in smart cities, termed Resource-Efficient Camouflaged Object Detection (RECOD). Detecting camouflaged objects is crucial for the proper functioning of various applications in smart cities, including traffic management, security surveillance, environmental monitoring, and infrastructure inspection. The proposed RECOD method is evaluated on four benchmark Camouflaged Object Detection datasets and a specialized image set of real-world camouflaged objects, demonstrating its potential to improve the efficiency of diverse real-world applications within the smart city context. Significantly, our study reveals that the proposed method not only achieves impressive results but a real-time performance of 70 FPS, rendering it well-suited for various real-world applications and potentially deployable on unmanned aerial vehicles. These findings indicate the significant utility of the RECOD method in detecting camouflaged objects and the potential to enhance the individual and overall functionality of smart cities.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".