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GithubWhether a fire extinguisher is in place often remains undetected until it is actually needed—by which time it is already too late. Relying on manual inspections results in low frequency and high risk of omissions, making sustained effectiveness difficult to achieve. OpenMV transforms fire safety inspections into an “always-on” process through visual monitoring.

In public and industrial venues such as shopping malls, industrial parks, factory workshops, and office buildings, the proper placement of fire extinguishers—whether they are positioned in accordance with regulations, whether they have been moved without authorization, and whether they are obstructed by debris—directly affects the efficiency of emergency response during unexpected fire incidents. Should a fire extinguisher be missing or unavailable for immediate use, the critical window for initial firefighting is often missed, resulting in greater safety losses.
Traditional fire safety inspections primarily rely on manual periodic checks, which not only limit inspection frequency but also heavily depend on inspectors’ sense of responsibility and experience, making omissions, misjudgments, or situations where “inspections are passed but equipment is subsequently removed” likely, thus hindering continuous and effective oversight.
The integration of SingTown’s OpenMV smart camera upgrades fire extinguisher inspections from manual patrols to round-the-clock, automated visual inspections.
The camera is permanently installed at the designated location corresponding to the fire protection equipment and operates stably over the long term, continuously capturing on-site images of the fire extinguisher area without requiring manual intervention. OpenMV runs object detection algorithms directly on the camera device to accurately identify the fire extinguisher itself and, in conjunction with predefined installation zone rules, determines whether the fire extinguisher is positioned as required. Additionally, through shape integrity and occlusion detection, the system can also identify whether the fire extinguisher is obstructed, toppled, or exhibits abnormal appearance, thereby assessing its operational readiness from multiple dimensions.
Once an anomaly such as a missing fire extinguisher, positional deviation, or obstruction is detected, OpenMV can immediately generate an alert signal at the edge, trigger on-site audible and visual alarm devices, or upload the event information to the management platform—achieving true “immediate anomaly detection and instant issue notification.”
Leveraging an edge deployment architecture and on-device AI capabilities, OpenMV can perform recognition and judgment without continuously uploading video streams—reducing network and storage costs while significantly enhancing system stability and response speed.
Fire inspection has thus been upgraded from “periodic manual checks” to “7×24-hour online monitoring,” effectively reducing fire safety risks and enhancing overall emergency response capabilities.

AI Sentinel Based on OpenMV: Automatic Alert for Unsecured Key Locations
Automatically detects whether the door of a key location has remained open for an extended period and promptly issues an alert.

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

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

Improper Storage of Gas Cylinders? Use OpenMV to Automatically Trigger Hazard Warnings
Automatically identifies unauthorized placements of gas cylinders to proactively detect gas-related safety hazards.

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

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