detections-semantic/_docs/00_research/03_comparison_framework.md

Comparison Framework

Selected Framework Type

Problem Diagnosis + Decision Support

Selected Dimensions

1. Memory Budget Feasibility
2. YOLO26/YOLOE-26 TRT Deployment Stability
3. YOLOE-26 Zero-Shot Accuracy for Domain
4. Path Tracing Algorithm Robustness
5. VLM Runtime & Integration Viability
6. Gimbal Control Adequacy
7. Training Data Realism
8. Security & Adversarial Resilience

Initial Population

Dimension	Draft01 Assumption	Researched Reality	Risk Level	Factual Basis
Memory Budget	YOLO + YOLOE-26 + CNN + VLM coexist on 8GB	Only ~5.2GB usable VRAM. Single YOLO TRT engine ~2.6GB. Two engines + CNN ≈ 5-6GB. No room for VLM simultaneously.	CRITICAL	Fact #1, #2, #3, #14, #19
YOLO26 TRT Stability	YOLO26-Seg TRT export assumed working	YOLO26 has confirmed confidence misalignment in TRT C++ and INT8 export crashes on Jetson. Active bugs unfixed.	HIGH	Fact #5, #6
YOLOE-26 Zero-Shot	Text prompts "footpath", "branch pile" assumed effective	Trained on LVIS/COCO. Military concealment is far OOD. No published domain benchmarks. Generic prompts may work for "footpath" but not "dugout" or "camouflage netting".	HIGH	Fact #7, #8
Path Tracing	Zhang-Suen skeletonization assumed robust	Classical skeletonization is noise-sensitive — spurious branches from noisy segmentation masks. GraphMorph/learnable skeletons are more robust alternatives.	MEDIUM	Fact #15, #16
VLM Runtime	vLLM or TRT-LLM assumed viable	TRT-LLM explicitly does not support edge devices. vLLM works but requires careful memory management. VLM cannot run concurrently with YOLO — must unload/reload.	HIGH	Fact #11, #12, #14
VLM Speed	UAV-VL-R1 ≤5s assumed	Cosmos-Reason2-2B: 4.7 tok/s on Orin Nano Super. For 50-100 token response: 10-21s. Significantly exceeds 5s target.	HIGH	Fact #13
Gimbal Control	PID assumed sufficient	PID works for stationary UAV. During flight, Kalman filter needed to compensate attitude/mounting errors. PID alone causes drift.	MEDIUM	Fact #17
Training Data	1500 images/class in 8 weeks assumed	Realistic for generic objects; challenging for military concealment (access, annotation complexity). Synthetic augmentation (GenCAMO, CamouflageAnything) can significantly help.	MEDIUM	Fact #18
Security	No security measures in draft01	Small edge YOLO models are more vulnerable to adversarial patches. Physical device capture risk (model weights, logs). PatchBlock defense available.	HIGH	Fact #9, #10