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Did you perform live-line work without wearing insulating gloves? OpenMV will immediately issue an alert!

At live-working sites, a single moment of negligence can lead to serious consequences. Relying solely on safety personnel for inspections makes it difficult to ensure continuous monitoring throughout the entire process. OpenMV enables the system to perform real-time verification of whether gloves are being worn.

绝缘手套佩戴识别

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-event inspections, making it difficult to achieve comprehensive, continuous, and standardized control.

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

Cameras can be deployed above work areas or at critical operation points; OpenMV continuously captures operational footage at the edge, locates personnel through human detection algorithms, and further performs fine-grained detection and tracking of hand regions.

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

When violations are detected, OpenMV can instantly output an alert signal at the edge to trigger on-site audible and visual alarms, voice prompts, or operational systems, enabling immediate intervention and preventing hazardous operations from continuing.

Since all identification and judgment are completed on the camera’s edge side, the system does not rely on cloud computing, ensuring 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 caused by human error.

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