Tech Stack Evaluation
Requirements Analysis
Functional Requirements
- Real-time open-vocabulary detection from UAV aerial imagery
- Footpath segmentation and path tracing with endpoint analysis
- Binary concealment classification on ROI crops
- On-demand VLM analysis for ambiguous detections
- Camera gimbal control with path-following
- Integration with existing Cython+TRT YOLO pipeline
Non-Functional Requirements
- Tier 1 inference ≤15ms, Tier 2 ≤200ms
- 5.2GB usable VRAM budget (Jetson Orin Nano Super 8GB)
- Field-deployable: thermal resilience, tamper protection
- Offline operation (no cloud dependency)
Constraints
- Jetson Orin Nano Super: 67 TOPS INT8, 8GB LPDDR5 unified, 68 GB/s bandwidth
- JetPack 6.2, CUDA 12.6, TensorRT 10.3
- Existing codebase: Cython + TensorRT (must extend, not replace)
- ViewPro A40 camera with ViewLink Serial Protocol V3.3.3
Technology Evaluation
Detection Framework
| Option |
Fitness |
Maturity |
Security |
Team Fit |
Cost |
Scalability |
Score |
| YOLOE-v8-seg (Ultralytics) |
9/10 — open-vocab + segmentation |
9/10 — YOLOv8 TRT proven |
7/10 — PatchBlock compatible |
9/10 — existing Cython+TRT expertise |
Free |
8/10 |
8.5 |
| YOLOE-26-seg (Ultralytics) |
10/10 — latest arch, NMS-free |
4/10 — TRT bugs on Jetson |
7/10 |
7/10 — new arch, less familiar |
Free |
9/10 |
6.5 |
| YOLO-World v2 |
7/10 — open-vocab, no seg |
7/10 — stable but older |
7/10 |
8/10 |
Free |
7/10 |
7.0 |
Selected: YOLOE-v8-seg. Upgrade path to YOLOE-26 when TRT issues resolved.
CNN Classifier
| Option |
Fitness |
Maturity |
Security |
Team Fit |
Cost |
Scalability |
Score |
| MobileNetV3-Small |
9/10 — binary classification, tiny |
10/10 — battle-tested |
8/10 |
9/10 |
Free |
8/10 |
9.0 |
| EfficientNet-B0 |
8/10 — slightly more accurate |
10/10 |
8/10 |
8/10 |
Free |
7/10 — larger |
8.0 |
| ResNet-18 |
7/10 — overkill for binary |
10/10 |
8/10 |
9/10 |
Free |
6/10 — 44MB |
7.5 |
Selected: MobileNetV3-Small. ~50MB TRT FP16. Best size/accuracy trade-off.
VLM
| Option |
Fitness |
Maturity |
Security |
Team Fit |
Cost |
Scalability |
Score |
| Moondream 0.5B INT4 |
7/10 — detect()/point() APIs |
7/10 — active development |
8/10 — local only |
7/10 — new, learning curve |
Free |
9/10 — 816 MiB |
7.5 |
| SmolVLM2-500M |
6/10 — no detect API |
6/10 — newer |
8/10 |
6/10 |
Free |
8/10 — 1.8GB |
6.5 |
| UAV-VL-R1 2B |
9/10 — aerial-specialized |
5/10 — not tested on Jetson |
8/10 |
5/10 |
Free |
4/10 — 2.5GB |
5.5 |
| No VLM (MVP) |
5/10 — no fallback |
10/10 |
10/10 |
10/10 |
Free |
10/10 |
8.0 |
Selected: Moondream 0.5B for VLM tier. "No VLM" as MVP fallback if Moondream insufficient.
VLM Runtime
| Option |
Fitness |
Maturity |
Security |
Team Fit |
Cost |
Scalability |
Score |
| ONNX Runtime |
8/10 — lightweight, cross-platform |
9/10 |
8/10 |
8/10 |
Free |
9/10 |
8.5 |
| vLLM |
7/10 — server-oriented, overkill for 0.5B |
8/10 — Jetson compatible |
7/10 |
6/10 — complex setup |
Free |
7/10 |
7.0 |
| PyTorch direct |
7/10 — simplest integration |
10/10 |
8/10 |
9/10 |
Free |
6/10 — no optimization |
7.5 |
| MLC-LLM |
6/10 — declining adoption |
5/10 |
7/10 |
5/10 |
Free |
7/10 |
5.5 |
Selected: ONNX Runtime for Moondream 0.5B. Lightweight, no server overhead.
Gimbal Control
| Option |
Fitness |
Maturity |
Security |
Team Fit |
Cost |
Scalability |
Score |
| filterpy (Kalman) + servopilot (PID) |
9/10 — cascade control |
8/10 — proven libraries |
8/10 |
7/10 — Kalman is new |
Free |
8/10 |
8.0 |
| Custom Kalman + PID from scratch |
8/10 |
5/10 — unproven |
8/10 |
6/10 |
Free |
7/10 |
6.5 |
| PID only (servopilot) |
6/10 — no drift compensation |
9/10 |
8/10 |
9/10 |
Free |
7/10 |
7.5 |
Selected: filterpy + servopilot cascade.
Adversarial Defense
| Option |
Fitness |
Maturity |
Security |
Team Fit |
Cost |
Scalability |
Score |
| PatchBlock |
9/10 — designed for edge YOLO |
7/10 — 2026 paper |
9/10 |
7/10 |
Free |
9/10 — CPU-based |
8.0 |
| Custom input validation |
5/10 — ad-hoc |
3/10 |
6/10 |
8/10 |
Free |
7/10 |
5.5 |
| None |
0/10 |
10/10 |
0/10 |
10/10 |
Free |
10/10 |
3.0 |
Selected: PatchBlock. Integrate as CPU preprocessing step.
Synthetic Data Generation
| Option |
Fitness |
Maturity |
Security |
Team Fit |
Cost |
Scalability |
Score |
| CamouflageAnything |
8/10 — CVPR 2025, camouflage-specific |
7/10 |
8/10 |
6/10 |
Free |
8/10 |
7.5 |
| GenCAMO |
8/10 — environment-aware, 2026 |
6/10 — newer |
8/10 |
6/10 |
Free |
8/10 |
7.0 |
| Cut-paste augmentation |
6/10 — simple but effective |
10/10 |
8/10 |
9/10 |
Free |
7/10 |
7.5 |
Selected: CamouflageAnything (primary) + cut-paste (supplementary).
Tech Stack Summary
| Layer |
Technology |
Version |
Justification |
| Hardware |
Jetson Orin Nano Super |
8GB |
Existing constraint |
| OS / SDK |
JetPack |
6.2 |
Latest for Orin Nano Super |
| GPU Runtime |
TensorRT |
10.3 (FP16) |
Existing pipeline, proven stability |
| Detection |
YOLOE-v8-seg |
Ultralytics ≥8.4 |
Stable TRT, open-vocab + segmentation |
| Classifier |
MobileNetV3-Small |
torchvision → TRT FP16 |
Tiny footprint, binary classification |
| VLM |
Moondream 0.5B |
INT4, ONNX |
816 MiB, detect()/point() APIs |
| VLM Runtime |
ONNX Runtime |
≥1.17 |
Lightweight, no server overhead |
| Path Tracing |
OpenCV + scikit-image |
OpenCV 4.x, skimage 0.22+ |
Preprocessing + skeletonization |
| Gimbal Kalman |
filterpy |
≥1.4 |
Kalman filter state estimation |
| Gimbal PID |
servopilot |
latest |
Anti-windup PID, dual-axis |
| Serial |
pyserial |
≥3.5 |
ViewLink protocol communication |
| Adversarial Defense |
PatchBlock |
2026 release |
CPU-based, edge-optimized |
| Synthetic Data |
CamouflageAnything |
CVPR 2025 |
Camouflage-specific generation |
| Encryption |
LUKS / dm-crypt |
Linux kernel |
Model weight encryption at rest |
| Core Language |
Cython + Python |
3.10+ |
Existing codebase extension |
Risk Assessment
| Technology |
Risk |
Mitigation |
| YOLOE-v8-seg |
Lower accuracy than YOLOE-26 |
Monitor YOLO26 TRT fix; upgrade when stable |
| Moondream 0.5B |
Untested for aerial concealment |
Empirical testing Week 8; fallback to no-VLM MVP |
| PatchBlock |
New (2026), limited field testing |
Can disable if causes false positives; low integration risk |
| filterpy Kalman |
Team unfamiliar |
Well-documented library; standard aerospace algorithm |
| CamouflageAnything |
Synthetic-to-real domain gap |
Supplement with real data; validate FP/FN rates |
| Demand-loaded VLM |
30-45s detection pause |
Batch requests; operator-triggered only; async notification |
| ONNX Runtime on Jetson |
Less optimized than TRT for vision models |
For 0.5B model, ONNX overhead is acceptable |
Learning Requirements
| Technology |
Effort |
Who |
| YOLOE visual prompts (SAVPE) |
Low — API-based |
Detection engineer |
| Moondream detect()/caption() |
Low — simple API |
ML engineer |
| filterpy Kalman filter |
Medium — state estimation theory |
Controls engineer |
| PatchBlock integration |
Low — preprocessing module |
Detection engineer |
| CamouflageAnything pipeline |
Medium — generative model setup |
Data engineer |
| LUKS encryption + secure boot |
Medium — Linux security |
DevOps / platform |
Oleksandr Bezdieniezhnykh