Model-device gap
Training environments and edge inference frameworks differ, requiring conversion, quantization and result review.
Industry Vision Solution / JISHIKEJI
Edge AI Vision Deployment SolutionVision System Development and Delivery
For local inference, low-latency recognition, onsite device linkage and offline operation, the solution covers model conversion, inference acceleration, edge hardware selection, application software, interface linkage and maintenance support.
HardwareRK3588 / Jetson / IPC
ModelONNX / RKNN / TensorRT
InterfaceRTSP / GPIO / PLC
Opslogs / config / upgrade
Edge AI Vision Deployment Solution / JIVISION
Industry Issues
Discuss Project →Typical Challenges
The solution is organized around business requirements, system modules, technical route and onsite delivery boundaries.
Limited onsite resources
Compute, memory, temperature, power and network conditions affect deployment.
Multi-stream pressure
RTSP, multi-camera and continuous inference require latency, frame-drop and resource control.
Maintainability needs visualization
Sites need parameter settings, logs, health status, version upgrade and exception recovery.
Solution Modules
Deployable Vision System Modules
Modules cover imaging, algorithms, software, interfaces, data and onsite maintenance so implementation scope can be defined clearly.
01
Edge hardware selection
Select hardware by camera count, resolution, model size, power and installation space.
02
Model conversion and acceleration
Support ONNX, RKNN, TensorRT deployment routes and inference-performance evaluation.
03
Multi-stream video access
Access RTSP, USB, GigE or local camera streams and manage frame rate.
04
Device linkage interface
Output GPIO, serial, TCP, Modbus, PLC or HTTP results.
05
Local application software
Provide configuration pages, live preview, alarm, logs and data query.
06
Operations and upgrade
Manage model versions, configuration backup, exception recovery and onsite iteration.
Applications
Applicable Scenarios
The solution maps typical stations, equipment, production lines and business scenarios.
IPC vision
Local multi-stream video access, model inference and platform linkage.
RK3588 edge box
Offline recognition, GPIO/serial output and lightweight deployment.
Jetson deployment
GPU inference, multi-model parallel processing and real-time video analytics.
Smart camera and device side
Compact, low-power and embedded onsite equipment deployment.
Hardware selection advice
Model conversion report
Inference deployment program
Video access module
Device linkage interface
Maintenance and upgrade plan
Before implementation, prepare inspection-object images or videos, qualified and defective samples, cycle-time requirements, installation space, communication interfaces and acceptance criteria. JIVISION will define the technical route, risk boundary and delivery scope based on samples and site conditions.
Implementation Path
From Scenario Review to Onsite Deployment
Each solution is implemented in stages based on inspection objects, onsite environment, hardware constraints and acceptance criteria.
01
Scenario ReviewProcess, targets and onsite environment02
Sample AnalysisImages, videos, good/bad samples and hard cases03
Solution DesignCameras, lighting, algorithms, platform and interfaces04
ValidationSample testing, metric review and risk confirmation05
IntegrationHardware/software debugging and interface connection06
IterationOnsite debugging, acceptance and data feedbackFAQ
Common Questions
These questions focus on scenarios, technical routes and implementation boundaries.
How is edge AI hardware selected?
Camera count, resolution, model complexity, latency, power, temperature and interface conditions should be considered.
Should converted models be rechecked?
Yes. Quantization, operator differences and inference-framework changes may affect results, so sample-set review is needed.
Can offline scenarios be deployed?
Yes. Local inference and event records can run onsite and synchronize results according to network conditions.
Contact
Submit Your Edge AI Vision Deployment Solution Requirement
Please provide the industry scenario, inspection target, speed and accuracy requirements, onsite environment, interface systems and available image or video samples.