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# Source Registry
## Source #1
- **Title**: Ultralytics YOLO26 Documentation
- **Link**: https://docs.ultralytics.com/models/yolo26/
- **Tier**: L1
- **Publication Date**: 2026-01-14
- **Timeliness Status**: Currently valid
- **Version Info**: YOLO26, Ultralytics 8.4.x
- **Summary**: Official YOLO26 docs — NMS-free, edge-first, MuSGD optimizer, improved small object detection, instance segmentation with semantic loss.
## Source #2
- **Title**: YOLOE: Real-Time Seeing Anything — Ultralytics Docs
- **Link**: https://docs.ultralytics.com/models/yoloe/
- **Tier**: L1
- **Publication Date**: 2025-2026
- **Timeliness Status**: Currently valid
- **Version Info**: YOLOE, YOLOE-26 (yoloe-26n-seg.pt through yoloe-26x-seg.pt)
- **Summary**: Official YOLOE docs — open-vocabulary detection/segmentation, text/visual/prompt-free modes, RepRTA, SAVPE, LRPC, zero inference overhead when re-parameterized.
## Source #3
- **Title**: YOLOE-26 Paper
- **Link**: https://arxiv.org/abs/2602.00168
- **Tier**: L1
- **Publication Date**: 2026-02
- **Timeliness Status**: Currently valid
- **Summary**: Integration of YOLO26 with YOLOE for real-time open-vocabulary instance segmentation. NMS-free, end-to-end.
## Source #4
- **Title**: Ultralytics YOLO26 Jetson Benchmarks
- **Link**: https://docs.ultralytics.com/guides/nvidia-jetson
- **Tier**: L1
- **Publication Date**: 2026
- **Timeliness Status**: Currently valid
- **Version Info**: YOLO11 benchmarks on Jetson Orin Nano Super, TensorRT FP16
- **Summary**: YOLO11n TensorRT FP16 on Jetson Orin Nano Super: 6.93ms at 640px. YOLO11s: 13.50ms. YOLO11m: 17.48ms.
## Source #5
- **Title**: Cosmos-Reason2-2B on Jetson Orin Nano Super
- **Link**: https://www.thenextgentechinsider.com/pulse/cosmos-reason2-runs-on-jetson-orin-nano-super-with-w4a16-quantization
- **Tier**: L2
- **Publication Date**: 2026-02
- **Timeliness Status**: Currently valid
- **Summary**: 4.7 tok/s on Jetson Orin Nano Super with W4A16 quantization.
## Source #6
- **Title**: UAV-VL-R1 Paper
- **Link**: https://arxiv.org/pdf/2508.11196
- **Tier**: L1
- **Publication Date**: 2025
- **Timeliness Status**: Currently valid
- **Summary**: Lightweight VLM for aerial reasoning. 48% better zero-shot than Qwen2-VL-2B. 2.5GB INT8, 3.9GB FP16. Open source.
## Source #7
- **Title**: SmolVLM 256M & 500M Blog
- **Link**: https://huggingface.co/blog/smolervlm
- **Tier**: L1
- **Publication Date**: 2025-01
- **Timeliness Status**: Currently valid
- **Summary**: SmolVLM-500M: 1.8GB GPU RAM, ONNX/WebGPU support, 93M SigLIP vision encoder.
## Source #8
- **Title**: Moondream 0.5B Blog
- **Link**: https://moondream.ai/blog/introducing-moondream-0-5b
- **Tier**: L1
- **Publication Date**: 2024-12
- **Timeliness Status**: Currently valid
- **Summary**: 500M params, 816 MiB INT4, detect()/point() APIs, Raspberry Pi compatible.
## Source #9
- **Title**: ViewPro ViewLink Serial Protocol V3.3.3
- **Link**: https://www.viewprotech.com/index.php?ac=article&at=read&did=510
- **Tier**: L1
- **Publication Date**: 2024
- **Timeliness Status**: Currently valid
- **Summary**: Serial command protocol for ViewPro gimbal cameras. UART 115200.
## Source #10
- **Title**: ArduPilot ViewPro Gimbal Integration
- **Link**: https://ardupilot.org/copter/docs/common-viewpro-gimbal.html
- **Tier**: L1
- **Publication Date**: 2025
- **Version Info**: ArduPilot 4.5+
- **Summary**: MNT1_TYPE=11 (Viewpro), SERIAL2_PROTOCOL=8, TTL serial, MAVLink 10Hz.
## Source #11
- **Title**: UAV-YOLO12 Road Segmentation
- **Link**: https://www.mdpi.com/2072-4292/17/9/1539
- **Tier**: L1
- **Publication Date**: 2025
- **Summary**: F1=0.825 for paths from UAV imagery. 11.1ms inference. SKNet + PConv modules.
## Source #12
- **Title**: FootpathSeg GitHub
- **Link**: https://github.com/WennyXY/FootpathSeg
- **Tier**: L3
- **Publication Date**: 2025
- **Summary**: DINO-MC pre-training + UNet fine-tuning for footpath segmentation. GIS layer generation.
## Source #13
- **Title**: Herbivore Trail Segmentation (UNet+MambaOut)
- **Link**: https://arxiv.org/pdf/2504.12121
- **Tier**: L1
- **Publication Date**: 2025-04
- **Summary**: UNet+MambaOut achieves best accuracy for trail detection from aerial photographs.
## Source #14
- **Title**: Open-Vocabulary Camouflaged Object Segmentation
- **Link**: https://arxiv.org/html/2506.19300v1
- **Tier**: L1
- **Publication Date**: 2025
- **Summary**: VLM + SAM cascaded approach for camouflage detection. VLM-derived features as prompts to SAM.
## Source #15
- **Title**: YOLO Training Best Practices
- **Link**: https://docs.ultralytics.com/yolov5/tutorials/tips_for_best_training_results
- **Tier**: L1
- **Publication Date**: 2025
- **Summary**: ≥1500 images/class, ≥10,000 instances/class. 0-10% background images. Pretrained weights recommended.
## Source #16
- **Title**: Jetson AI Lab LLM/VLM Benchmarks
- **Link**: https://www.jetson-ai-lab.com/tutorials/genai-benchmarking/
- **Tier**: L1
- **Publication Date**: 2025-2026
- **Summary**: Llama-3.1-8B W4A16 on Jetson Orin Nano Super: 44.19 tok/s output, 32ms TTFT. vLLM as inference engine.
## Source #17
- **Title**: servopilot Python Library
- **Link**: https://pypi.org/project/servopilot/
- **Tier**: L3
- **Publication Date**: 2025
- **Summary**: Anti-windup PID controller for gimbal control. Dual-axis support. Zero dependencies.
## Source #18
- **Title**: Multi-Model AI Resource Allocation for Humanoid Robots: A Survey on Jetson Orin Nano Super
- **Link**: https://dev.to/ankk98/multi-model-ai-resource-allocation-for-humanoid-robots-a-survey-on-jetson-orin-nano-super-310i
- **Tier**: L3
- **Publication Date**: 2025
- **Summary**: Running VLA + YOLO concurrently on Orin Nano Super is "mostly theoretical". GPU sharing causes 10-40% latency jitter. Needs lighter edge-optimized models.
## Source #19
- **Title**: TensorRT Multiple Engines on Single GPU
- **Link**: https://github.com/NVIDIA/TensorRT/issues/4358
- **Tier**: L2
- **Publication Date**: 2025
- **Summary**: NVIDIA recommends single engine with async CUDA streams over multiple separate engines. CUDA context push/pop needed for multiple engines.
## Source #20
- **Title**: TensorRT High Memory Usage on Jetson Orin Nano (Ultralytics)
- **Link**: https://github.com/ultralytics/ultralytics/issues/21562
- **Tier**: L2
- **Publication Date**: 2025
- **Summary**: YOLOv8-OBB TRT engine consumes ~2.6GB on Jetson Orin Nano. cuDNN/CUDA binary loading adds ~940MB-1.1GB overhead per engine.
## Source #21
- **Title**: NVIDIA Forum: Jetson Orin Nano Super Insufficient GPU Memory
- **Link**: https://forums.developer.nvidia.com/t/jetson-orin-nano-super-insufficient-gpu-memory/330777
- **Tier**: L2
- **Publication Date**: 2025-04
- **Summary**: Orin Nano Super shows 3.7GB/7.6GB free GPU memory after OS. Even 1.5B Q4 model fails to load due to KV cache buffer requirements (model weight 876MB + temp buffer 10.7GB needed).
## Source #22
- **Title**: YOLO26 TensorRT Confidence Misalignment on Jetson
- **Link**: https://www.hackster.io/qwe018931/pushing-limits-yolov8-vs-v26-on-jetson-orin-nano-b89267
- **Tier**: L2
- **Publication Date**: 2026
- **Summary**: YOLO26 exhibits bounding box drift and inaccurate confidence scores when converted to TRT for C++ deployment on Jetson. YOLOv8 works fine. Architecture-specific export issue.
## Source #23
- **Title**: YOLO26 INT8 TensorRT Export Fails on Jetson Orin (Ultralytics Issue #23841)
- **Link**: https://github.com/ultralytics/ultralytics/issues/23841
- **Tier**: L2
- **Publication Date**: 2026
- **Summary**: YOLO26n INT8 TRT export fails with checkLinks error during calibration on Jetson Orin with TensorRT 10.3.0 / JetPack 6.
## Source #24
- **Title**: PatchBlock: Lightweight Defense Against Adversarial Patches for Edge AI
- **Link**: https://arxiv.org/abs/2601.00367
- **Tier**: L1
- **Publication Date**: 2026-01
- **Summary**: CPU-based preprocessing module recovers up to 77% model accuracy under adversarial patch attacks. Minimal clean accuracy loss. Suitable for edge deployment.
## Source #25
- **Title**: Qrypt Quantum-Secure Encryption for NVIDIA Jetson Edge AI
- **Link**: https://thequantuminsider.com/2026/03/12/qrypt-quantum-secure-encryption-nvidia-jetson-edge-ai/
- **Tier**: L2
- **Publication Date**: 2026-03
- **Summary**: BLAST encryption protocol for Jetson Orin Nano and Thor. Quantum-secure end-to-end encryption, independent key generation.
## Source #26
- **Title**: Adversarial Patch Attacks on YOLO Edge Deployment (Springer)
- **Link**: https://link.springer.com/article/10.1007/s10207-025-01067-3
- **Tier**: L1
- **Publication Date**: 2025
- **Summary**: Smaller YOLO models on edge devices are more vulnerable to adversarial attacks. Trade-off between latency and security.
## Source #27
- **Title**: Synthetic Data for Military Camouflaged Object Detection (IEEE)
- **Link**: https://ieeexplore.ieee.org/document/10660900/
- **Tier**: L1
- **Publication Date**: 2024
- **Summary**: Synthetic data generation approach for military camouflage detection training.
## Source #28
- **Title**: GenCAMO: Environment-Aware Camouflage Image Generation
- **Link**: https://arxiv.org/abs/2601.01181
- **Tier**: L1
- **Publication Date**: 2026-01
- **Summary**: Scene graph + generative models for synthetic camouflage data with multi-modal annotations. Improves complex scene detection.
## Source #29
- **Title**: Camouflage Anything (CVPR 2025)
- **Link**: https://openaccess.thecvf.com/content/CVPR2025/html/Das_Camouflage_Anything_...
- **Tier**: L1
- **Publication Date**: 2025
- **Summary**: Controlled out-painting for realistic camouflage dataset generation. CamOT metric. Improves detection baselines when used for fine-tuning.
## Source #30
- **Title**: YOLOE Visual+Text Multimodal Fusion PR (Ultralytics)
- **Link**: https://github.com/ultralytics/ultralytics/pull/21966
- **Tier**: L2
- **Publication Date**: 2025
- **Summary**: Multimodal fusion of text + visual prompts for YOLOE. Concat mode (zero overhead) and weighted-sum mode (fuse_alpha). Merged into Ultralytics.
## Source #31
- **Title**: Learnable Morphological Skeleton for Remote Sensing (IEEE TGRS 2025)
- **Link**: https://ui.adsabs.harvard.edu/abs/2025ITGRS..63S1458X
- **Tier**: L1
- **Publication Date**: 2025
- **Summary**: Learnable morphological skeleton priors integrated into SAM for slender object segmentation. Addresses downsampling information loss.
## Source #32
- **Title**: GraphMorph: Topologically Accurate Tubular Structure Extraction
- **Link**: https://arxiv.org/pdf/2502.11731
- **Tier**: L1
- **Publication Date**: 2025
- **Summary**: Branch-level graph decoder + SkeletonDijkstra for centerline extraction. Reduces false positives vs pixel-level segmentation.
## Source #33
- **Title**: UAV Gimbal PID Control for Camera Stabilization (IEEE 2024)
- **Link**: https://ieeexplore.ieee.org/document/10569310/
- **Tier**: L1
- **Publication Date**: 2024
- **Summary**: PID controllers applied in gimbal construction for stabilization and tracking.
## Source #34
- **Title**: Kalman Filter Steady Aiming for UAV Gimbal (IEEE)
- **Link**: https://ieeexplore.ieee.org/ielx7/6287639/10005208/10160027.pdf
- **Tier**: L1
- **Publication Date**: 2023
- **Summary**: Kalman filter + coordinate transformation eliminates attitude and mounting errors in UAV gimbal. Better accuracy than PID alone during flight.
## Source #35
- **Title**: vLLM on Jetson Orin Nano Deployment Guide
- **Link**: https://learnopencv.com/deployment-on-edge-vllm-on-jetson/
- **Tier**: L2
- **Publication Date**: 2026
- **Summary**: vLLM can run 2B models on Orin Nano 8GB. Shared memory must be increased to 8GB. Memory management critical.
## Source #36
- **Title**: Jetson Orin Nano LLM Bottleneck Analysis
- **Link**: https://ericxliu.me/posts/benchmarking-llms-on-jetson-orin-nano/
- **Tier**: L2
- **Publication Date**: 2025
- **Summary**: Bottleneck is memory bandwidth (68 GB/s), not compute. Only 5.2GB usable VRAM after OS overhead. 40 TOPS largely underutilized for LLM inference.
## Source #37
- **Title**: TRT-LLM: No Edge Device Support Statement
- **Link**: https://github.com/NVIDIA/TensorRT-LLM/issues/7978
- **Tier**: L1
- **Publication Date**: 2025
- **Summary**: TensorRT-LLM developers explicitly state they do not aim to support edge devices/platforms.
## Source #38
- **Title**: Qwen3-VL-2B on Orin Nano Super (NVIDIA Forum)
- **Link**: https://forums.developer.nvidia.com/t/performance-inquiry-optimizing-qwen3-vl-2b-inference-for-2-qps-target-on-orin-nano-super/359639
- **Tier**: L2
- **Publication Date**: 2026
- **Summary**: Performance inquiry for Qwen3-VL-2B targeting 2 QPS on Orin Nano Super. Indicates active community attempts to deploy 2B VLMs on this hardware.
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