DeepRodent is a unified, detector-agnostic deep learning framework built for precise, generalizable, multi-task rodent monitoring in laboratory environments. It jointly predicts:
📦 Standard object detection
🔄 Oriented Bounding Boxes (OBB) — captures rotation during grooming, rearing, and huddling
🎨 Pixel-level instance segmentation — accurate boundary masks even under occlusion
🧭 Temporal-consistent behavioral embeddings — stable identity tracking across video frames
Rather than bolting these on separately, DeepRodent fuses them into a single-stage, multi-head architecture — so one forward pass gives you everything downstream biology pipelines need.
Instance segmentation isolates each animal precisely
Lighting/cage geometry varies across labs
Cross-domain robustness constraint for generalization
Jittery masks across video frames
Temporal consistency loss stabilizes identity tracking
Need for downstream analytics
Direct output → trajectories, heatmaps, behavior states
🏗️ Architecture Snapshot
Input Frame → Shared Backbone → ┬── Detection Head
├── OBB Head (θ-aware)
├── Instance Seg Head
└── Temporal Embedding Head
↓
Trajectory Tracking · Behavior States · Occupancy Heatmaps
Detector-agnostic: plug into YOLOv8 → YOLO12 families and gain +2.6 to +3.1 mAP consistently. 🔌
📊 Benchmarks
Evaluated on a private multi-setting laboratory rodent dataset (~30K annotated frames, 60/20/20 split):
🥇 Outperforms RT-DETR by +6.7 mAP₅₀₋₉₅
🥇 Outperforms Mask R-CNN by +13.5 mAP₅₀₋₉₅
⚡ Maintains 154 FPS — real-time, deployment-ready
Full ablation, cross-domain, and SOTA comparison tables are in the paper.
DeepRodent is an assistive research tool — it does not replace veterinary oversight or trained behavioral experts. Developed following ARRIVE guidelines and the 3Rs principles (Replacement, Reduction, Refinement). Please validate performance before deployment in a new laboratory setting.
📜 Citation
If DeepRodent helps your research, please cite:
bibtex
1@article{panboonyuen2026deeprodent,
2 title = {DeepRodent: A Robust and Generalizable Vision Framework for Automated Rodent Monitoring in Experimental Biology},
3 author = {Panboonyuen, Teerapong},
4 year = {2026}
5}
Made with 🧡 for open, reproducible science.
⭐ If this helped your research, consider starring the GitHub repo!
