HarmonyOS × domestic NPU × 2026 vision foundation models
Eyes for the production line — it sees, thinks, and learns on its own.
We package open-source HarmonyOS, domestic vision NPUs, and the most advanced vision foundation models into an end-to-end quality-inspection pipeline — from 16K line-scan cameras and edge inference boxes, to natural-language-promptable VLM inference, all the way to closed-loop linkage with MES/SCADA/security centers. It spots the defect and explains why.
Why Build Machine Vision as IoT
Traditional machine vision is a one-off project — retrain when the product changes, drift when the lighting changes. We turn it into a self-evolving Internet of Things.
Pain · Rigid Rules
Each category needs thousands of samples, and every product change means halting the line to retrain. When a new defect appears the model doesn't recognize it, so you fall back on scheduled manual inspection.
Turning Point · Language-Promptable
YOLO-World and Grounding DINO turn the detection target into a single sentence: "find scratches, find oil stains." With VLM on the edge, workers converse with the model.
Result · Self-Learning Fleet
HarmonyOS turns each camera into a node; missed-detection samples flow back to the center · auto fine-tuning every week · versions pushed to the whole fleet.
Domestic Vision Embedded NPU Matrix
From 5 TOPS entry-level to 128 TOPS automotive-grade · all domestic chips · 3 of them have completed official HarmonyOS Std-5.0 adaptation, the rest supported via our BSP porting.
RK3588
WorkhorseRK3576
ValueAX650N
Strong ViTSG2300X
Multi-StreamJourney6
Auto-GradeA311D2
EntryRecommended models: lightweight lines pick RK3576/A311D2 · multi-class defects pick RK3588 · heavy models + multi-stream pick AX650N / SG2300X · safety fencing + automotive-grade pick Journey 6.
Frontier Vision Model Matrix
Four lines advance in parallel: real-time detection, segmentation, VLM, and anomaly detection — all deployed quantized on edge NPUs.
Real-Time Detection
From fixed classes to open vocabulary · RT-DETR true real-timeSegmentation
SAM family on the edge · one click to segmentVLM · It Talks
Line workers ask in plain language — it sees, it answersAnomaly Detection
No defect samples needed · zero-shot go-liveThe real inflection point is natural-language-promptable edge quality inspection: a worker types "find scratches and oil stains," and YOLO-World / Grounding-DINO-Edge respond zero-shot; 3B-class VLMs (Qwen2.5-VL / MiniCPM-V) run directly on RK3588 / AX650N, generating a descriptive report for every defect image. Synthetic-defect Diffusion + fleet feedback compress go-live from weeks to days.
Three-Layer Collaborative Architecture
Model · NPU · HarmonyOS — one toolchain all the way to the field
Zhejiang Seehoo · Textile Defect Detection
The textile customer's chronic headache is fabric defects: holes, weft skew, oil stains, color deviation, and spots — a manual inspector covers at most 3,000 m a day, and the miss rate spikes once eyes tire. With Seehoo, we replaced traditional inspection on three production lines: 16K line-scan cameras + RK3588 edge boxes + HarmonyOS 5.0 unified reporting + YOLOv12s + EfficientAD dual-model fusion.
- ①6 defect classes · holes / weft skew / oil stains / color deviation / spots / end-out, full coverage at 40 m/min line speed
- ②7-day cold start · YOLO-World zero-shot labeling + synthetic-defect Diffusion first, then backfill with real samples
- ③MES linkage · defect coordinates fed straight back to the host system for auto meter-marking / scrapping / traceability
- ④Continuous evolution · missed samples flow back weekly, model versions pushed to the fleet via HarmonyOS OTA
—— Seehoo Production Lead
Beijing Ski Simulator · Electronic Fence + Vision Fence
An indoor ski simulator runs at high speed; a user falling, sliding off the board, or wandering into the tail end can all cause serious accidents. The traditional approach only uses emergency-stop buttons and light curtains, with no awareness of the "on the machine but losing balance" scenario. For the Beijing customer we built a triple-decision system: an electronic fence (safety light curtain + mmWave radar) + a vision fence (stereo camera + YOLO-World + pose estimation), all centrally controlled by HarmonyOS 5.0.
- ①Triple fence · light-curtain millisecond cutoff + mmWave coarse range + vision fine pose
- ②YOLO-World + pose · four event types: fall / out-of-bounds / third-party intrusion / abnormal posture
- ③Decision within 80 ms · RK3576 edge inference + HarmonyOS soft bus driving the motor brake
- ④Graded response · alert = audible-visual warning · danger = slow down · critical = emergency stop
—— Beijing Customer · Safety Manager
From the First Image to Mass-Production Go-Live · 6 Steps
In DevEco we package this chain into a CI/CD pipeline — 7-day cold start.
On-Site Capture
On-board camera + HarmonyOS HDF unified capture format · automatic logging
Labeling · Prompting
Traditional labeling ✕ + YOLO-World zero-shot masking + T-Rex Label visual prompting
Training · Synthesis
Few-shot fine-tuning + synthetic-defect Diffusion · 7-day cold start
Quantized Deployment
Pulsar2 / RKNN / TPU-MLIR quantize to INT8/INT4 with one command
Edge Inference
On HarmonyOS devices · NPU inference + VLM anomaly explanation
Continuous Learning
Missed samples flow back · weekly auto fine-tuning · fleet-wide model versioning
All These Sites Can Go Live
Same architecture · swap the model · swap the light source · swap the HarmonyOS HDF driver, for fast replication.
Textiles · Dyeing & Printing
Holes / weft skew / oil stains / color deviation · high-precision line scan · MES linkage
Safety · Fencing
Light curtain + vision + mmWave triple decision · fall / out-of-bounds detection
3C · PCB
Solder joints / missing parts / polarity / scratches · multi-light switching · sub-pixel level
Food · Agriculture
Foreign-object rejection · appearance grading · ripeness assessment · packaging verification
Turn the Site into an IoT that Sees, Thinks, and Self-Learns
Give us a production line, an image, or a video clip — we package domestic NPUs, the HarmonyOS edge layer, and 2026 frontier vision models into a deployable solution: 7-day cold start, 3-month closed-loop go-live.
HarmonyOS × Domestic NPU × 2026 Vision Foundation Models
Eyes for the production line — it sees, thinks, and learns on its own.
Domestic NPU Matrix
RK3588 / RK3576 · AX650N · SG2300X · Journey 6 · A311D2 —— 5-128 TOPS coverage.
2026 Model Matrix
YOLOv12 · YOLO-World · SAM family · Qwen2.5-VL · EfficientAD / Dinomaly.
Three-Layer Architecture
Case · Zhejiang Seehoo Textiles
16K line scan + RK3588 + YOLOv12s + EfficientAD · 6 defect classes · miss rate < 0.3% · payback in 3 months.
Case · Beijing Ski Simulator Fencing
Light curtain + mmWave + vision triple decision · RK3576 edge inference · motor linkage within 80 ms · 0 false stops.
Book a Production-Line POCFAQ
What can the machine-vision inspection system detect?
Deep-learning-based surface-defect recognition, dimensional measurement, OCR, and assembly verification, with millisecond inference.
Which production-line scenarios does it suit?
Suited for in-line inspection on electronics, auto-parts, and pharmaceutical-packaging lines.
How do you deploy it onto an existing line?
We support integration with existing lines / MES. Contact Qingdao Huo15 (phone 18554898815, email support@huo15.com) for model selection and a solution.