Performing live-line work without wearing insulating gloves? OpenMV issues an immediate alert!

At live-working sites, a single moment of negligence can lead to serious consequences. Relying solely on safety officers for inspections makes it difficult to achieve continuous, real-time monitoring. OpenMV enables the system to determine in real time whether gloves are being worn.

Correctly wearing insulating gloves during power maintenance, live-line work, and equipment maintenance is a critical prerequisite for ensuring personnel safety. However, in actual operational scenarios, traditional supervision methods largely rely on on-site monitoring by safety officers or post-incident inspections, making it difficult to achieve comprehensive and continuous regulatory control.

Based on SingTown Technology’s OpenMV smart camera, the system can automatically and in real time identify whether personnel are wearing their personal protective equipment, upgrading safety inspections from “manual monitoring” to “system-based perception.”

The camera can be deployed above the work area or at critical operation points. OpenMV continuously captures operational footage at the edge, locates personnel through a human detection algorithm, and further performs fine-grained detection and tracking of hand regions.

By integrating colour features, shape contours, and a glove-wearing status recognition model, the system can accurately determine whether personnel are wearing insulating gloves and identify instances of non-wearing or improper wearing.

When violations are detected, OpenMV can instantly output alarm signals on-device to trigger on-site audio-visual alarms, voice prompts, or operational systems for immediate intervention and prevention of hazardous operations.

As all identification and judgment are performed on the camera’s edge side, the system does not rely on cloud computing, enabling fast response times and stable deployment—making it especially suitable for environments with extremely high requirements for real-time performance and reliability, such as power field sites. Leveraging OpenMV’s edge-side AI vision capabilities, safety protection shifts from “relying on experience and manual effort” to “relying on data and systems”, significantly reducing safety risks arising from human error.