detections-semantic/_docs/00_research/02_fact_cards.md

Fact Cards

Fact #1

• Statement: Jetson Orin Nano Super has 7.6GB total unified memory, but only ~3.7GB free GPU memory after OS/system overhead in a Docker container.
• Source: Source #21
• Phase: Assessment
• Target Audience: Edge AI multi-model deployment on Orin Nano Super
• Confidence: ✅ High
• Related Dimension: Memory contention

Fact #2

• Statement: A single TensorRT engine (YOLOv8-OBB) consumes ~2.6GB on Jetson Orin Nano. cuDNN/CUDA binary loading adds ~940MB-1.1GB overhead per engine initialization.
• Source: Source #20
• Phase: Assessment
• Target Audience: TRT multi-engine memory planning
• Confidence: ✅ High
• Related Dimension: Memory contention

Fact #3

• Statement: Running VLA + YOLO detection concurrently on Orin Nano Super is described as "mostly theoretical" in 2025 surveys. GPU sharing causes 10-40% latency jitter.
• Source: Source #18
• Phase: Assessment
• Target Audience: Multi-model concurrent inference
• Confidence: ⚠️ Medium (survey, not primary benchmark)
• Related Dimension: Memory contention, performance

Fact #4

• Statement: NVIDIA recommends using a single TRT engine with async CUDA streams over multiple separate engines for GPU efficiency. Multiple engines need CUDA context push/pop management.
• Source: Source #19
• Phase: Assessment
• Target Audience: TRT engine management
• Confidence: ✅ High
• Related Dimension: Memory contention, architecture

Fact #5

• Statement: YOLO26 exhibits bounding box drift and inaccurate confidence scores when deployed via TensorRT on Jetson Orin Nano in C++. This is an architecture-specific export issue not present in YOLOv8.
• Source: Source #22
• Phase: Assessment
• Target Audience: YOLO26/YOLOE-26 TRT deployment
• Confidence: ✅ High
• Related Dimension: YOLOE-26 viability, deployment risk

Fact #6

• Statement: YOLO26n INT8 TensorRT export fails during calibration graph optimization on Jetson Orin with TensorRT 10.3.0 / JetPack 6. ONNX export succeeds but TRT build crashes.
• Source: Source #23
• Phase: Assessment
• Target Audience: YOLO26 edge deployment
• Confidence: ✅ High
• Related Dimension: YOLOE-26 viability, deployment risk

Fact #7

• Statement: YOLOE supports multimodal fusion of text + visual prompts with two modes: concat (zero overhead) and weighted-sum (fuse_alpha). This can improve robustness over text-only or visual-only prompts.
• Source: Source #30
• Phase: Assessment
• Target Audience: YOLOE prompt strategy
• Confidence: ✅ High
• Related Dimension: YOLOE-26 accuracy

Fact #8

• Statement: YOLOE text prompts are trained on LVIS (1203 categories) and COCO. Military concealment classes (dugouts, branch camouflage, FPV hideouts) are far out-of-distribution from training data. No published benchmarks for this domain.
• Source: Sources #2, #3 (inferred from training data descriptions)
• Phase: Assessment
• Target Audience: YOLOE-26 zero-shot accuracy
• Confidence: ⚠️ Medium (inference from known training data)
• Related Dimension: YOLOE-26 accuracy

Fact #9

• Statement: Smaller YOLO models (commonly used on edge devices) are more vulnerable to adversarial patch attacks than larger counterparts, creating a latency-security trade-off.
• Source: Source #26
• Phase: Assessment
• Target Audience: Edge AI security
• Confidence: ✅ High
• Related Dimension: Security

Fact #10

• Statement: PatchBlock is a lightweight CPU-based preprocessing module that recovers up to 77% of model accuracy under adversarial patch attacks with minimal clean accuracy loss.
• Source: Source #24
• Phase: Assessment
• Target Audience: Edge AI adversarial defense
• Confidence: ✅ High
• Related Dimension: Security

Fact #11

• Statement: TensorRT-LLM developers explicitly stated they "do not aim to support models on edge devices/platforms" when asked about VLM support on Orin NX.
• Source: Source #37
• Phase: Assessment
• Target Audience: VLM runtime selection
• Confidence: ✅ High
• Related Dimension: VLM integration

Fact #12

• Statement: vLLM can deploy 2B models on Jetson Orin Nano 8GB. Shared memory must be increased to 8GB. Memory management is critical. Bottleneck is memory bandwidth (68 GB/s), not compute (67 TOPS).
• Source: Sources #35, #36
• Phase: Assessment
• Target Audience: VLM runtime on Jetson
• Confidence: ✅ High
• Related Dimension: VLM integration

Fact #13

• Statement: Cosmos-Reason2-2B achieves 4.7 tok/s on Jetson Orin Nano Super with W4A16 quantization. Llama-3.1-8B W4A16 achieves 44.19 tok/s (text-only). VLMs are significantly slower due to vision encoder overhead.
• Source: Sources #5, #16
• Phase: Assessment
• Target Audience: VLM inference speed estimation
• Confidence: ✅ High
• Related Dimension: VLM integration, performance

Fact #14

• Statement: A 1.5B Q4 model on Jetson Orin Nano Super failed to load because KV cache temp buffer required 10.7GB while only 6.5GB was available. Model weights alone were only 876MB.
• Source: Source #21
• Phase: Assessment
• Target Audience: VLM memory management
• Confidence: ✅ High
• Related Dimension: Memory contention, VLM integration

Fact #15

• Statement: Morphological skeletonization suffers from noise-induced boundary variations causing spurious skeletal branches. Recent methods (2025) use scale-space hierarchical simplification for controllable robustness.
• Source: Source #31 (related search results)
• Phase: Assessment
• Target Audience: Path tracing robustness
• Confidence: ✅ High
• Related Dimension: Path tracing

Fact #16

• Statement: GraphMorph (2025) operates at branch-level using Graph Decoder + SkeletonDijkstra, producing topology-aware centerline masks. Reduces false positives vs pixel-level segmentation approaches.
• Source: Source #32
• Phase: Assessment
• Target Audience: Path extraction algorithms
• Confidence: ✅ High
• Related Dimension: Path tracing

Fact #17

• Statement: Kalman filter + coordinate transformation in UAV gimbal systems eliminates attitude and mounting errors that PID controllers alone cannot compensate for during flight.
• Source: Source #34
• Phase: Assessment
• Target Audience: Gimbal control algorithm
• Confidence: ✅ High
• Related Dimension: Gimbal control

Fact #18

• Statement: Synthetic data generation for camouflage detection is a validated approach: GenCAMO (2026) uses scene graphs + generative models; CamouflageAnything (CVPR 2025) uses controlled out-painting. Both improve detection baselines.
• Source: Sources #28, #29
• Phase: Assessment
• Target Audience: Training data strategy
• Confidence: ✅ High
• Related Dimension: Training data

Fact #19

• Statement: Usable VRAM on Jetson Orin Nano Super is approximately 5.2GB after OS overhead (not the advertised 8GB). The 8GB is shared between CPU and GPU.
• Source: Source #36
• Phase: Assessment
• Target Audience: Memory budget planning
• Confidence: ✅ High
• Related Dimension: Memory contention

Fact #20

• Statement: FP8 quantization for Qwen2-VL-2B performs worse than FP16 on vLLM. INT8/W4A16 are the recommended quantization formats for 2B VLMs on constrained hardware.
• Source: vLLM Issue #9992
• Phase: Assessment
• Target Audience: VLM quantization strategy
• Confidence: ✅ High
• Related Dimension: VLM integration