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# Hardware
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- Jetson Orin Nano Super, 67 TOPS INT8, 8GB shared LPDDR5
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- YOLO model consumes approximately 2GB RAM; remaining ~6GB available for semantic detection + VLM
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- FP16 precision for all models
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# Camera
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- Primary: ViewPro A40 — 1080p (1920x1080), 40x optical zoom, f=4.25-170mm, Sony 1/2.8" CMOS (IMX462LQR)
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- Alternative: ViewPro Z40K (higher cost)
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- Thermal sensor available (640x512, NETD ≤50mK) — not a core requirement, potential future enhancement
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- Output: HDMI or IP, 1080p 30/60fps
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# Operational
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- Flight altitude: 600–1000 meters
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- All seasons: winter (snow), spring (mud), summer (vegetation), autumn — phased rollout starting with winter
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- All terrain types: forest, open field, urban edges, mixed
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- ViewPro A40 zoom transition time: 1–2 seconds (physical constraint, 40x optical zoom traversal)
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- Level 1 to Level 2 scan transition: ≤2 seconds including physical zoom movement
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# Software
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- Must extend the existing Cython + TensorRT codebase in the detections repository
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- Inference engine: TensorRT on Jetson, ONNX Runtime fallback
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- Model input resolution: 1280px (matching existing pipeline)
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- Existing tile-splitting mechanism available for large images
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- VLM must run locally on Jetson (no cloud connectivity assumed)
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- VLM runs as separate process with IPC (not compiled into Cython codebase)
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- YOLO and VLM inference must be scheduled sequentially (shared GPU memory, no concurrent execution)
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# Integration
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- Existing detections service: FastAPI + Cython, deployed as Docker container on Jetson
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- Must consume YOLO detection output (bounding boxes with class, confidence, normalized coordinates)
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- Must output bounding boxes with coordinates in the same format for operator display
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- GPS coordinates provided by separate GPS-denied system — not in scope
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# Project Scope
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- Annotation tooling, training pipeline, data collection automation — out of scope (separate repositories)
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- GPS-denied navigation — out of scope (separate project)
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- Mission planning / route selection — out of scope (existing system)
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