Hydra-Mask-RCNN: An Adaptive HydraNet Architecture for Autonomous Aerial Vehicle Object Detection
Bibliographic record
Abstract
Environmental monitoring is essential for understanding and mitigating the impact of human activities on the planet, as well as for developing effective strategies for sustainable development and conservation. Accurate object detection in aerial images is crucial for environmental monitoring and surveillance using autonomous aerial vehicles (AAVs). However, existing methods, including Mask R-CNN (MRCNN) and you-only-look-once (YOLO), struggle to detect small- and medium-sized objects from AAV sensors, limiting their usability for AAV surveillance. We propose Hydra-MRCNN (HMRCNN), a multitask learning network that enhances detection precision for small- and medium-sized objects in aerial images. By integrating an adaptive branching network (ABN) with HydraNet, HMRCNN improves feature extraction and object detection capabilities. Evaluations on Microsoft Common Objects in Context (MS-COCO), Aerial-Cars, VisDrone, and Plastic in River datasets show significant improvements in average recall (AR) compared to baseline models, including MRCNN and YOLO. Our approach has important implications for environmental monitoring, enabling more accurate detection of objects relevant to transportation, security, traffic, pollution, and infrastructure management. With the growing use of AAVs in environmental surveillance, HMRCNN offers a valuable tool for enhancing environmental measurement and assessment capabilities. Our method improves detection performance by over 6% on AAV datasets, making it a valuable contribution to the field as the commercial AAV market is expected to grow from 25 billion to 50 billion in the next decade.
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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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".