Eliminate manual monitoring of mask-wearing—OpenMV uses AI vision for automatic detection

In public places with high personnel turnover, whether masks are worn correctly directly affects on-site order and management efficiency. Relying solely on manual monitoring is not only costly but also difficult to ensure timely coverage of all entrances. OpenMV shifts the assessment of “whether masks are properly worn” to the on-site location, enabling more timely and stable management.

In key areas such as public spaces, 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 protocol implementation.

Traditional approaches primarily rely on manual monitoring or post-event review of surveillance footage, resulting in high labour costs and difficulty in timely intervention. Some cloud-based video analytics solutions also face issues such as strong network dependency, high latency, and privacy risks. Leveraging SingTown’s OpenMV intelligent cameras, the system performs real-time mask-wearing status recognition at the camera edge, shifting judgment, decision-making, and control to the site to achieve true “instant perception and instant response.”

OpenMV is typically installed above entry and exit gates, passageways, or key areas to capture frontal or three-quarter view images of individuals entering.

The camera runs a facial detection algorithm on the edge device 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 edge-based AI classification model, it accurately distinguishes between multiple states, including 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. This transforms mask management from “manual supervision” to an intelligent model of “automatic sensing + automatic execution.”