detections-semantic/_docs/00_research/01_source_registry.md

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.