Let OpenMV’s AI vision automatically determine whether a person is wearing a mask—no more manual monitoring required.

In public places with high personnel turnover, whether masks are worn properly directly affects on-site order and management efficiency. Relying solely on manual monitoring is not only costly but also makes it difficult to cover all entrances promptly. OpenMV moves the judgment of “whether a mask is worn correctly” to the site, enabling more timely and stable management.

In key areas such as public places, entrances and exits of industrial parks, hospitals, and transportation hubs, whether personnel wear masks correctly directly affects on-site order management and the efficiency of safety regulation enforcement.

Traditional methods primarily rely on manual monitoring or reviewing surveillance footage after incidents occur—resulting in high labor costs and delayed intervention. Some cloud-based video analytics solutions also face challenges such as strong network dependency, high latency, and privacy risks. Leveraging SingTown’s OpenMV intelligent cameras, the system performs real-time mask-wearing status detection directly at the camera edge, shifting judgment, decision-making, and control to the site—enabling true “instant perception and instant response.”

OpenMV is typically installed above entrances and exits, over passageways, or over key areas to capture frontal or three-quarter view images of individuals entering.

The camera runs a facial detection algorithm on the device side to automatically locate facial regions, and further performs feature extraction and structural analysis on key areas such as the mouth and nose. By integrating an on-device AI classification model, it accurately distinguishes between multiple states: not wearing a mask, wearing a mask improperly (exposing the nose or mouth), and wearing a mask correctly.

When the system detects a violation, OpenMV can directly trigger voice prompt devices, access control systems, or local alarm modules via GPIO or serial interfaces, enabling immediate alerts upon detection and immediate interception upon violation—without requiring manual intervention.

Leveraging OpenMV’s on-device AI and edge computing architecture, the recognition process does not require uploading video data to the cloud—ensuring millisecond-level response times, significantly reducing network and server costs, and safeguarding personnel privacy at the source; thus, mask management evolves from “manual supervision” to an intelligent model of “automatic perception + automatic execution.”