Computer vision · PPE safety detection
Your site has cameras on every corner and still misses PPE violations on the night shift
Banao builds PPE safety detection systems that monitor every worker in every camera zone, every shift — identifying missing hard hats, absent safety vests, bare hands in glove-required areas, and unprotected eyes before a violation becomes a reportable incident.
We deliver a working system: trained models, camera integration, a real-time alert pipeline, and the per-frame audit log your safety manager and auditor need. Built to the production standard we hold the AI that runs our own company to.
What a Banao PPE detection system monitors
A PPE compliance system is more than a model. It is the camera coverage map, the trained detectors for each PPE type, the zone rules, the alert pipeline, and the audit trail — we build and own all of them.
Hard hat and helmet detection
Frame-by-frame detection of whether each worker in frame is wearing a hard hat — on construction sites, process plants, and factory floors where head-strike risk is constant.
Safety vest and hi-vis detection
Identification of safety vests and high-visibility jackets in each zone, including distinguishing between partial and full compliance, and flagging workers who are present without one.
Safety goggles and face shield detection
Detection of eye protection in grinding, chemical-handling, and other zones where the requirement is non-negotiable and spot-checks are the only current method.
Gloves and hand protection detection
Identification of gloves at close-work camera positions — welding, chemical contact, and assembly areas — where bare-handed contact is the most common fine-grade violation.
Safety footwear detection
Detection of appropriate safety boots and toe caps in zones where footwear requirements differ from the wider site, including segregated areas with chemical or heavy-drop hazards.
Harness and fall-arrest detection
Visual confirmation that harness and fall-arrest equipment is worn in elevated work zones — the check that matters most when working at height and that a ground-level walkthrough cannot do.
Zone-based PPE rule enforcement
Different zones carry different PPE requirements. We map your site's zones, assign the right PPE rules to each camera, and alert on the violations that apply per zone — not a single global rule.
Real-time alert and audit pipeline
Violations surface to a control room or safety officer within seconds: a frame capture, the zone, the missing item, and the timestamp — every detection logged for the audit trail.
Why camera coverage does not equal PPE compliance coverage
A site with 40 cameras has 40 feeds that nobody can watch continuously. Safety walkthroughs cover the site once per shift, and the violation that happens between rounds — a worker who removed their hard hat in the heat, a contractor who entered the wrong zone — goes unrecorded until something happens. The camera saw it and nobody was watching.
PPE detection turns passive recording into active monitoring. A model watches every feed in real time, flags the frame where the violation occurs, and routes an alert with the evidence before the worker reaches the hazard. The cameras you already have start doing the work a site walk cannot — continuous, recorded, and with a time-stamped frame that is admissible in an incident investigation.
Continuous, not periodic
A walkthrough is a sample; a model is a census. Every worker in every camera frame is checked, not just the zones a safety officer happened to pass through on their round.
Evidence in the frame
When a violation alert fires, the captured frame goes with it — date, time, zone, and the missing PPE item visible in the image. That frame is the evidence record if the incident is investigated or disputed.
Alert before the harm, not after
A post-shift report tells you where violations occurred. A real-time alert gives your safety officer the chance to intervene before the worker reaches the hazard — the distinction that changes incident outcomes.
Works on the cameras already installed
We deploy on your existing CCTV or IP camera infrastructure where image quality allows, adding detection capability to hardware you have already bought and run.
What makes PPE detection hard on a real site — and how we handle it
A lab demo for PPE detection runs on a clean image of one worker in good light, wearing or not wearing a single, photogenic piece of equipment. A real construction site or process plant gives you 15 workers in frame, overlapping bodies, mixed lighting that changes by hour and season, helmets of six different colours from three contractors, and a camera angle that was designed for perimeter security, not PPE inspection.
Almost every failure we have seen in PPE detection pilots comes from ignoring the hard cases and testing only the easy ones. The model that works on the demo fails on night-shift images, on partial occlusion, on workers at the edge of the frame. We build for the hard cases from the start, because those are the ones your incident log is full of.
Multi-worker, occluded scenes
We train and validate on the crowded frames your site actually produces — workers overlapping, partially occluded, at the edge of coverage — not on the single-person ideal case.
Lighting variation across shifts
We capture training data across day, dusk, and night conditions on your specific cameras, so the model that works on the morning shift still works when the lighting changes.
Multi-contractor, multi-colour PPE
Safety vests come in orange, yellow, and lime. Hard hats come in white, yellow, blue, and red. We build detectors that generalise across the colour palette your site actually uses, not a single-colour lab set.
Camera angle and resolution constraints
We assess every camera angle for PPE detectability before we commit. If an angle cannot reliably resolve the PPE items being checked, we say so — and recommend where to add a targeted camera rather than promising accuracy a bad angle cannot support.
We run AI in production at Banao before we build it for anyone else
Banao is a ~300-person engineering company. InterviewGod screens our own hires every week; Vikaas runs our own demand generation in production. Neither is a safety camera — but both are AI that has to be right on real, unpredictable inputs, monitored for drift, and trusted by our own team, or it gets replaced.
That operating standard is what we bring to a PPE detection system. The model is tested on your specific site's footage before go-live. The alert pipeline has a failure mode you agreed to. The audit trail is built for your safety function, not an afterthought. We hold the work to the bar we hold our own AI to.
- InterviewGodAI we built and run on Banao's own hiring, tested on real applicants every week.
- VikaasAI we built and run on Banao's own demand generation, live in production daily.
When computer vision is the wrong answer for PPE monitoring
PPE detection is a genuine, proven application of computer vision — and also one where a poor implementation produces alerts nobody acts on, which is worse than no system at all. We will tell you the limits before you spend a budget on them:
- Camera angles were never designed for inspection: a perimeter camera at 8 metres looking down at 45 degrees was designed to see faces, not the top of a hard hat. Retrofitting PPE detection to badly-placed cameras usually fails; we assess every angle before we commit.
- The site is too dark for detection: if night-shift footage is unlit or under-lit, a model cannot reliably detect PPE — the answer is a lighting change or a different sensor, not a model promise we cannot back with numbers.
- Too few non-compliance examples to train or validate on: if PPE violations on your site are so rare that you cannot build a validation set, you cannot trust an accuracy claim. In that case, anomaly detection on the feed may be a better approach than a direct PPE classifier.
- Full-body PPE in dense scenes: facilities where every worker is in full chemical suits, clean-room gowns, or reflective gear create occlusion and appearance-overlap that defeats most detectors. We test before we promise.
- Alert fatigue from low-precision detections: a system that fires alerts on shadows, scaffolding, and background objects trains your safety team to ignore it. Precision matters as much as recall — we hold both to a written target before go-live.
How we start — test detection on your worst camera before we build the system
PPE detection pilots fail when they run on the best camera, in the best light, with the most compliant workers. We test on the opposite.
- AI Discovery Sprint2 weeks · fixed price
We take footage from your hardest camera — lowest light, most crowded frame, worst angle — and test whether a PPE detector can meet your required precision and recall on that footage. You get a feasibility verdict, a camera coverage assessment, and ROI maths — yours to keep regardless of what you decide next. If you proceed, the Sprint cost is credited against the build.
- Build and integrate
We train the detectors on your site's own footage and PPE palette, build the alert pipeline and audit log, wire into your existing control room or safety management system, and validate to a written acceptance test before go-live.
- Production monitoring and retraining
We monitor precision and recall in production, route low-confidence frames to a human reviewer, and run a retraining cycle when conditions shift — new contractor PPE colours, a camera move, a lighting change — so the system stays accurate rather than quietly decaying.
Frequently asked questions
What is PPE safety detection?
It is using computer-vision models on camera feeds to automatically identify whether workers are wearing required personal protective equipment — hard hats, safety vests, goggles, gloves, footwear, harnesses — and to fire an alert and log the frame when a violation is detected, continuously and without a human watching every feed.
How accurate is computer vision for PPE compliance monitoring?
On well-lit, reasonably-angled camera feeds with a trained detector, precision and recall both above 90% are achievable for common PPE items like hard hats and safety vests. Accuracy drops in poor light, at distance, and under heavy occlusion. We set a written precision and recall target for your site before build and hold the system to it in acceptance testing — not a general benchmark claim.
Can it work on our existing CCTV cameras?
Often yes, with caveats. We assess each camera's angle, height, resolution, and lighting before committing. Cameras installed for perimeter security or general surveillance are sometimes usable; some angles produce images where PPE detection is not reliable. We tell you which cameras can support detection and where a targeted additional camera would make the difference.
Which PPE items can it detect?
Hard hats, safety vests, high-visibility jackets, safety goggles, face shields, gloves, safety boots, and harnesses are all detectable when the camera angle and image quality are sufficient. We build per-item detectors calibrated to your site's PPE palette, not a generic model trained on stock images.
How quickly does it alert when a violation is detected?
Alerts are generated within seconds of the frame being captured — detection runs on the live feed, not a recording review. The alert goes to your control room or safety officer with the captured frame, zone, timestamp, and missing item identified, so the response can start before the worker reaches the hazard.
How does it handle multiple workers and crowded frames?
We train and validate on multi-worker frames from your specific site — workers overlapping, partially occluded, and at varying distances. The acceptance test includes crowded frames, not just single-worker images. Accuracy drops with heavy occlusion, and we document the floor on that in the acceptance criteria rather than hiding it.
What happens to the footage and violation data?
We deploy so footage and detection logs stay inside the jurisdiction your policy or regulation requires — UAE, Saudi Arabia, India, the UK, or the US — and we build an audit trail of every detection with the frame, timestamp, and zone for your safety and compliance teams.
How long to go from contract to live detection?
A common path is a 2-week Discovery Sprint to prove feasibility on your footage, then a build and integration of 6–10 weeks covering model training, alert pipeline, and acceptance testing. Banao's engineering bench means work starts in weeks. The timeline depends on the number of PPE types, cameras, and the complexity of your zone rules.
Show us the camera feed your safety team cannot watch
Whether it is the night-shift floor, the back corner of a busy zone, or the access point where workers remove PPE in the heat — in 45 minutes we will tell you whether detection is feasible on that footage and what building the system would take.
Book a 45-min scoping call