Published: 2026-07-29 | English reference version of the Chinese source article.
A front-end device for busy industrial views
Factories, stations, industrial parks and public facilities already have cameras. The difficult part is turning a busy picture into a record that an operator can review and act on. Forklifts and people meet at loading bays. Conveyor areas collect material. At fuel unloading points, wheel chocks, static grounding leads and warning signs have to be checked in the right sequence.
The XINHUO AI camera released for these scenes uses an eight-core CPU, a 3 TOPS INT8 NPU and 8 MP image capture. The device performs image capture, local inference, rule evaluation, event capture and structured event output at the point of installation.
How many tasks can one camera carry
Where picture quality, algorithm load and real-time requirements allow it, one camera can run up to ten algorithms in parallel. This is a project configuration rather than a universal promise. Resolution, analysis frame rate, target density, the number of tasks in one view, live preview and event-delivery frequency all change the real workload.
The practical value is that a focused point can combine the tasks its operator needs, such as a vehicle in a defined area, human entry, a lingering object, smoke or a camera obstruction. Before use, the chosen combination needs a continuous test on the real camera view, including resource use, event delay, stream recovery and duplicate-event handling.
Camera placement comes before algorithm selection
A high, wide camera can show the relationship between people, vehicles and a boundary. It may not show a helmet, phone, hand motion or goggle clearly enough. A close view at a loading bay can show a wheel chock or a grounding lead but cannot replace a wide road view for traffic statistics.
XINHUO AI uses a downward viewing angle of about 10 to 30 degrees, installation height of about 2 to 10 metres and a target larger than 100 by 100 pixels as initial survey references. They are not fixed acceptance limits. Lens focal length, lighting, the task and the actual area decide the final position.
Rules turn recognition into an event
Detecting a forklift is a visual result. A forklift entering a pedestrian exclusion area and remaining there for a defined period is an operational event. The same applies to vehicle parking, intrusion, people gathering and fire-route occupation. A rule needs a region, effective time, duration, evidence requirement and duplicate-event interval.
The event sent to a platform can include the device, channel, region, time, event type, target attributes, target crop, full-scene image and state. These fields let an operator review the record and let the platform avoid writing the same ongoing event repeatedly.
Custom work for site-specific targets
A standard model cannot cover every work process, piece of equipment, sign, uniform or product defect. XINHUOAI is used to organize site material, train on GPUs, evaluate a model and export it to an AI camera, AI Edge Box or AI NVR.
Useful training material includes normal work, abnormal events and confusing conditions such as glare, steam, dust, rain, night lighting, occlusion and similar backgrounds. After a model is exported, the team still checks device compatibility, resource use, input and output fields, then tests the model at the installed point.