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GithubAt live-working sites, a single moment of negligence can lead to severe consequences. Relying solely on safety personnel for inspections makes it extremely difficult to achieve continuous, real-time monitoring. OpenMV enables the system to perform real-time judgment on whether gloves are being worn.

Properly 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 and continuous regulatory control.
Based on SingTown’s OpenMV intelligent camera, the system can automatically and in real time identify whether workers are wearing their 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 color features, shape contours, and a 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 alarm signals on the edge side to trigger on-site audio-visual alarms, voice prompts, or operational systems, enabling immediate intervention and preventing hazardous operations from continuing.
Since 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 labor” to “relying on data and systems,” significantly reducing safety risks caused by human error.

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Personnel crossing the boundary triggers an alarm; OpenMV interprets the “sense of security boundary.”
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“AI Urban Management Officer” is Here: An Automatic Vendors-Encroaching-on-Pavement Recognition System Built on OpenMV
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Improper Storage of Gas Cylinders? Use OpenMV to Automatically Trigger Hazard Warnings
Automatically identifies unauthorized placements of gas cylinders to proactively detect potential gas-related safety hazards.

Did you perform live-line work without wearing insulating gloves? OpenMV issues an immediate alert!
Automatically identifies whether workers are wearing insulating gloves to assist in safe operations.

Leverage the OpenMV smart camera to detect surface defects on aluminum plates in real time
Online identification of defects on aluminum plate surfaces, such as scratches and dents, to support quality control.