AI algorithm

Helmet and Reflective Vest Detection

AI algorithm for detecting helmet and reflective vest compliance in factories, construction sites, warehouses, parks, and industrial roads.

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Helmet and vest detection works by reviewing the visible head and upper-body region of a person in a configured work area. It is useful for entrances, workshop aisles, loading areas, construction work zones, and other places where a clear PPE rule applies.

Typical Output

The system can output event type, snapshot, full-scene image, camera name, time, detection zone, and review status. Alarm intervals and duration rules reduce repeated alerts.

Deployment Advice

Before deployment, confirm camera angle, target pixel size, worker movement direction, illumination, and whether the detection region should exclude public roads or non-work areas. A person who is too distant, backlit, obscured, or turned away should not be treated as a reliable acceptance sample.

Algorithm deployment notes

How to set up helmet and reflective vest checks that can be reviewed

PPE detection is a visual check of whether a person in a defined camera view appears to have the agreed helmet or reflective vest feature. It is not a complete judgement about that person’s safety. The camera, rule, and evidence record should therefore be designed around a visible PPE decision.

Use an approach view where possible

Entry lanes, gates, walkways, turnstiles, and controlled transitions often provide a clearer head-and-upper-body view than a distant overview camera. Check target size, viewing angle, backlight, head position, clothing colour, crowding, and whether workers pass the camera for long enough to obtain a usable image.

State what counts as a non-compliance event

Agree the monitored area, PPE type or colour where relevant, dwell period, time schedule, alert interval, and who can acknowledge an event. A helmet carried in the hand, severe occlusion, a dark background, or a view with insufficient head detail may need a review rule rather than an automatic conclusion.

Store the context with the detection

The event record should contain the camera, time, region, detection type, snapshot, and related recording where available. This gives a supervisor enough context to review the observed PPE condition and understand where it occurred.

Test each representative route

Acceptance footage should include the actual shifts, weather or lighting changes, different uniforms, movement direction, and busy periods. Results from one clear camera view should not be applied automatically to every entrance or work area on the site.

Related planning: Multi-algorithm AI camera deployment. The final rule should match the site PPE standard and the intended response procedure.

Project inquiry

Tell us your camera environment and required AI analytics.

For the first version this site uses a lightweight email-based inquiry flow. It avoids a database and keeps the English site simple until inquiry volume justifies a dedicated CRM integration.