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

In power maintenance, live-line work, and equipment maintenance, the correct wearing of insulating gloves 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 compliance control.
Based on SingTown’s OpenMV smart camera, the system can automatically and in real time identify whether personnel are wearing 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 colour features, shape contours, and a wear-status recognition model, the system can accurately determine whether personnel are wearing insulating gloves and identify cases of non-wear or improper wear.
Upon detecting a violation, OpenMV can instantly output an alert signal at the edge to trigger on-site audio-visual alarms, voice prompts, or operational systems, enabling immediate intervention and preventing hazardous operations from continuing.
As all identification and judgment are performed on the camera’s edge side, the system does not rely on cloud computing, ensuring rapid response 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 has shifted from “relying on experience and manual effort” to “relying on data and systems”, significantly reducing safety risks arising from human error.

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Personnel crossing boundary triggers an alarm; OpenMV interprets the “sense of security boundary”
Detecting personnel or equipment crossing virtual perimeter lines.

“AI Urban Management Officer” is Here: An Automated Street Vending Detection System Built on OpenMV
Automatically identifies illegal street vending activities to support daily urban governance.

Improper placement of gas cylinders? Use OpenMV to automatically trigger hazard alerts
Automatically identifies non-compliant placement positions of gas cylinders to proactively detect potential gas safety hazards.

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

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