Perception
OmniVision and other CMOS sensors; TOF / optical flow / stereo modules for stable, clear capture.
Covers video doorbells, peephole cameras, optical flow / TOF / visual tracking product combinations. In 2025–2026 we strengthen edge AI and sensor–ISP–SoC coordination for face recognition, human detection, and spatial sensing in production.
We deliver more than algorithm demos: imaging chain, board engineering, and model deployment as one pilot-ready, regression-ready path.
System flow
From photons to events: full edge imaging chain
A mass-production AI imaging pipeline split into six coordinated stages along signal and compute flow.
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01
Sensor
OV / OX CMOS
Optional TOF / optical flow -
02
ISP imaging
AWB / AE / HDR
Noise reduction and color baseline -
03
SoC / NPU
Encode, preview
Edge inference compute -
04
AI model
YOLO / MobileNet
Quantization and on-board deployment -
05
Event policy
Human / package / intrusion
Triggers and power scheduling -
06
Connectivity & delivery
Wi-Fi / streaming / app
Pilot and mass-production regression
Solution stack
Four layers working together
Imaging
ISP tuning (AWB / AE / HDR / noise reduction) for OEM-grade image quality baseline.
Intelligence
YOLO / MobileNet edge models: training, quantization, NPU deployment, and power scheduling.
Product
Doorbells, IPC, industrial and robotics vision modules—mechanical, firmware, and pilot production in one delivery.
Implementation path
Six steps from requirements to mass production
Typical projects advance by phase to shorten the cycle where “algorithms run but hardware is hard to ship.”
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1
Scene and metrics definition
Define detection classes, false-alarm rate, latency, power, and resolution; lock target form factors (doorbell / IPC / industrial).
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2
Sensor and platform selection
OV / OX selection matched to SoC / NPU compute and MIPI bandwidth; add TOF / optical flow when needed.
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3
PCBA and ISP bring-up
Schematic / layout, imaging parameter calibration, reproducible imaging baseline.
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4
Algorithm training and quantization
Train detection models on scene data; INT8 quantization and on-board operator adaptation.
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5
System integration and pilot run
Firmware, event policy, wireless, and app integration; small-batch pilot for consistency.
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6
Mass-production regression and iteration
Version control, production sampling, and continuous model upgrades; domestic and overseas delivery windows.
Reference case
Product example: smart video doorbell SHT-H301
The full chain applied to a mass-production home doorbell—a closed loop of perception, imaging, and edge AI.
Product capabilities
- Smart video doorbell with fingerprint / PIN and on-device face unlock
- On-device face detection / matching—less cloud dependency and mistaken unlock risk
- Combines with peephole, Wi-Fi, and app remote management for residential entry
How this solution maps to the product
- Sensor + ISP: HDR and low-light tuning for doorway backlight and night conditions
- Edge model: Lightweight face model on board; network only on events
- Full product delivery: Mechanical fit, firmware integration, pilot to small batch
Customer value
- Shorter path from demo to sellable finished product
- Imaging and algorithms tuned by one team—lower multi-vendor handoff risk
- Extensible to peephole, IPC, TOF, and related product lines
More scenarios
Extensions of the same pipeline
Security IPC
Zone intrusion, human tracking, multi-stream preview—reuse doorbell imaging and model assets.
Service robots
Multi-camera sync, TOF / optical flow fusion for navigation, obstacle avoidance, and follow modes.
Line vision
Industrial PCBA plus detection models for AOI and warehouse vision.
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