Computer Vision for Construction Safety: A Practical Implementation Guide

Construction remains one of the most dangerous industries in Australia. Safe Work Australia's latest figures put the fatality rate at 3.0 per 100,000 workers, roughly three times the national average across all industries. We've spent decades improving safety through better processes, training, and regulation. Those efforts have worked. But we're hitting diminishing returns with traditional approaches, and that's exactly where computer vision steps in.

I've been involved in deploying CV safety systems on sites ranging from mid-rise residential builds in Melbourne to tier-one infrastructure projects in Queensland. The technology works. But the gap between "the technology works" and "the technology delivers value on your site" is where most implementations either succeed or quietly die. This guide covers what I've learned about closing that gap.

What Computer Vision Actually Detects

Modern CV safety systems go well beyond the "is that person wearing a hard hat?" demos you've probably seen at trade shows. Production systems today handle multiple detection categories simultaneously, running inference on edge devices at 15-30 frames per second.

PPE Compliance

The most mature use case. Current models reliably detect hard hats, high-visibility vests, safety glasses, gloves, and steel-cap boots. Detection accuracy for hard hats and hi-vis is consistently above 95% in good conditions. Smaller items like safety glasses are harder, typically landing around 85-90% accuracy, though this improves with higher-resolution cameras and better mounting positions.

The real value isn't catching individuals. It's identifying patterns. When your system shows that PPE compliance drops 40% after lunch on Fridays, or that a particular subcontractor consistently enters zones without proper gear, you've got actionable intelligence that no number of safety walks would surface.

Exclusion Zone Monitoring

This is where CV starts saving lives, not just preventing injuries. Defining virtual exclusion zones around heavy plant, open excavations, live overhead work, and crane swing paths means you can detect unauthorised entry in real time. The system alerts the relevant supervisor, logs the event, and in some configurations triggers audible warnings on site.

On a recent infrastructure project, exclusion zone monitoring caught an average of twelve unauthorised entries per week during the first month of operation. Twelve near-misses per week that previously went unnoticed. By month three, that number dropped to two. People changed their behaviour because they knew the system was watching.

Near-Miss Detection

This is the frontier. CV systems can now identify proximity events, workers getting too close to moving plant, objects falling from height near personnel, vehicles reversing toward pedestrians. These aren't binary detections; the system calculates trajectories and closing speeds to assess risk levels.

Near-miss data is gold for safety teams. Traditional near-miss reporting relies on workers self-reporting, which captures maybe 5-10% of actual events. CV-based detection doesn't have ego, embarrassment, or time pressure. It captures everything.

The Numbers That Matter

Across deployments I've been involved with and published case studies from peers in the industry, the metrics cluster around consistent ranges:

• 30-60% reduction in recordable safety incidents within the first 12 months of deployment. The wide range reflects differences in baseline safety maturity. Sites with poor existing safety culture see the biggest improvements.
• 70-80% reduction in PPE non-compliance events once workers understand the system is active. Behavioural change is the primary driver, not punitive enforcement.
• 90%+ reduction in exclusion zone breaches within three months. This is the most consistent metric across deployments.
• 2-4x increase in near-miss reporting volume from CV detection compared to manual reporting alone.

The ROI case isn't hard to make. A single serious injury on an Australian construction site costs between $500,000 and $2 million when you factor in direct costs, project delays, regulatory investigation, and insurance impacts. A comprehensive CV safety system for a mid-size project costs $80,000-$150,000 per year. Preventing one serious incident pays for the system several times over.

Integration with BIM and Digital Twins

The most sophisticated deployments don't treat CV as a standalone safety tool. They integrate it with the project's Building Information Model to create a living digital twin of site safety conditions.

When CV detections are mapped to BIM coordinates, you get spatial analytics. Which areas of the site have the highest density of safety events? How do incident patterns change as the build progresses through different phases? Where should you position additional barriers or signage based on actual movement patterns rather than assumptions?

BIM integration also enables predictive capabilities. If the model shows that structural steel erection on Level 4 starts next week, and historical data from Levels 1-3 shows a spike in exclusion zone breaches during steel erection, you can pre-position resources and brief teams before the risk materialises.

We typically integrate via IFC exports and standard APIs. Most major BIM platforms (Autodesk Construction Cloud, Trimble Connect, Bentley iTwin) support the data flows needed. The engineering isn't trivial, but it's well-understood.

Australian WHS Compliance

Computer vision doesn't replace your WHS obligations. It strengthens your ability to meet them. Under the model WHS Act adopted across most Australian jurisdictions, a PCBU must ensure, so far as is reasonably practicable, the health and safety of workers. "Reasonably practicable" is doing a lot of work in that sentence.

As CV technology matures and becomes more accessible, the argument that deploying it isn't reasonably practicable gets harder to sustain. Regulators are paying attention. SafeWork NSW has published guidance noting the potential of AI-powered monitoring systems. WorkSafe Victoria has funded pilot programs. The trajectory is clear.

There are also privacy considerations under WHS law and the Privacy Act. Workers must be informed that CV monitoring is in operation. Most jurisdictions require consultation with health and safety representatives before implementing surveillance technologies. Data retention policies need to comply with relevant privacy legislation. None of this is prohibitive, but it needs to be handled properly.

A well-implemented CV system actually helps with regulatory compliance in several ways:

• Evidence of due diligence. Continuous, documented monitoring demonstrates that you're actively managing safety risks, not just writing policies about them.
• Incident investigation. When incidents do occur, CV footage and detection logs provide objective evidence that's invaluable during regulatory investigations.
• Audit readiness. Automated compliance reporting means you can demonstrate your safety performance at any time, not just when you've had notice of an audit.

Implementation: Getting It Right

The technical deployment is the easy part. Cameras, edge compute, network connectivity, cloud analytics, these are solved problems. What determines success or failure is everything around the technology.

Start with a specific problem. Don't try to detect everything on day one. Pick the safety risk that's costing you the most, usually PPE compliance or exclusion zone breaches, and nail that first. Expand once you've proven value and built operational muscle.

Involve your safety team from the start. If your safety managers learn about the CV system from an IT project update, you've already failed. They need to shape the detection rules, define the alert workflows, and own the operational response. Technology without operational integration is just expensive CCTV.

Get the camera positions right. This sounds obvious but it's the most common source of poor performance. Mounting height, angle, field of view, lighting conditions, and occlusion from temporary structures all affect detection accuracy. Budget time for a proper site survey and expect to adjust positions as the build evolves.

Plan for connectivity. Construction sites aren't data centres. Network coverage is patchy, bandwidth is limited, and infrastructure moves constantly. Edge computing, where inference runs on devices near the cameras rather than in the cloud, is essential for real-time detection. Cloud connectivity is needed for analytics and dashboards, but the core safety function should work even when the internet doesn't.

Manage the people side. Workers will push back if they perceive CV as surveillance rather than safety. The framing matters enormously. This is a tool that protects them, not one that watches them. Early engagement, transparent communication about what's collected and how it's used, and demonstrating that the system catches hazards (not just people) all help build acceptance.

Where This Is Heading

The next generation of CV safety systems will integrate with wearables, IoT sensors, and autonomous plant to create genuinely proactive safety ecosystems. We're already seeing early versions: smart watches that receive CV-triggered alerts when the wearer enters a hazard zone, autonomous haul trucks that adjust speed based on CV-detected pedestrian proximity, crane anti-collision systems that fuse CV with LiDAR for centimetre-accurate awareness.

The construction industry has been slower than mining to adopt these technologies, but the gap is closing fast. With Australian WHS regulators increasingly recognising AI-powered safety tools as part of "reasonably practicable" measures, the question isn't whether to deploy computer vision on your sites. It's how quickly you can do it well.