AuraSense ships as a C++ SDK with Python bindings. Embed neuromorphic crack detection into your drone inspection platform in days, not months. P95 <5ms on CPU-only hardware.
A four-stage pipeline from raw drone frames to structured crack detection events. All on-device, all real-time.
Push frames via RTSP, RTMP, or the C++ push API. The engine ingests at up to 30fps. Pass a FrameBuffer pointer directly from your camera driver for zero-copy ingestion.
Load a JSON config to select active STDP lanes (1–6), set crack score thresholds, enable degraded-mode policy, and configure output destinations (callback, WebSocket, or file log).
Every processed frame emits a structured event: crack_score, risk_score, frame_id, roi, and per-lane breakdown. Subscribe via C++ callback or read from the Python binding.
Pipe events to your inspection dashboard, a WebSocket ground station stream, or the bundled Streamlit dashboard. Trigger alerts at custom risk thresholds. Log to JSON for audit trails.
Every processed frame emits a FRAME_LOG event your application can consume via C++ callback, Python binding, or WebSocket stream.
| Field | Type | Description |
|---|---|---|
frame_id |
uint64 | Monotonic frame counter from engine start |
crack_score |
float [0,1] | Crack detection confidence for this frame |
risk_score |
float [0,1] | Fused risk scalar — threshold this to trigger alerts |
roi |
BoundingBox | Pixel-space region of interest (x, y, w, h) |
alarm |
bool | True when risk_score exceeds your configured threshold |
The AuraSense SDK runs on standard x86-64 and ARM64 Linux hardware. No CUDA, no NPU, no special silicon required.
Intel NUC, mini-PC, or any Linux laptop. Requires AVX2. Tested on Core i5/i7 and Ryzen 5/7. P95 <5ms on 4-core systems at 30fps.
NVIDIA Jetson Orin Nano (CPU only, no CUDA), Raspberry Pi 5, or any ARM64 SBC running Ubuntu 20.04+. Ideal for tight payload budgets.
Linux (Ubuntu 20.04+ or Debian 11+). 4 GB RAM minimum. 8 GB recommended for concurrent inference + logging. GCC 10+ or Clang 12+.
SFSVC deploys wherever conventional connectivity fails and inspection stakes are highest.
Detect hairline cracks, spalling, and water damage on high-rise exteriors without scaffolding. Inspect 30 floors in under an hour.
Continuous monitoring of bridge decks, piers, and expansion joints. SFSVC identifies sub-millimeter cracks invisible to the human eye.
Automated surface inspection for airports. Detect FOD, cracks, and pavement degradation at taxiway speeds without halting operations.