BACKGROUND

To shift the safety paradigm from reactive (analysing accidents after they happen) to predictive (analysing near-misses to prevent future accidents). The goal is to automatically detect, log, and analyse high-risk operational behaviours and scenarios, providing data-driven insights to mitigate risks before they result in injury or damage.

SIGNIFICANCE OF PROBLEM

Accurately defining and programming the system to understand the complex and dynamic context of a “near miss” in a busy port. The computational load required for real-time analysis of extensive video data.

POTENTIAL MARKET SIZE

Estimating the exact global market size for AI and analytics strictly dedicated to port worker safety requires triangulating data from the broader Smart Ports and Automated Terminal markets. Because safety solutions are often integrated into larger automation and operational technology (OT) upgrades, the market breaks down as follows:

• Global Smart Ports Market: Projected to reach between USD 3.83 billion and USD 5.35 billion in 2026 , driven heavily by demands for higher cargo throughput and decarbonization.
• AI and Analytics Sub-segment: AI, IoT, and process automation account for a significant share of port technology deployments. Software and digital-twin platforms are projected to advance at a massive 26% CAGR . The core addressable market for port-focused AI and data analytics sits between USD 1 billion and USD 1.5 billion for 2026.
• Worker Safety Niche: Nested within this software and analytics layer is the specialized safety segment. This encompasses AI-driven CCTV for PPE detection, drone-based hazard inspections, and spatial analytics to prevent human-machine collisions.

This specialized sub-market represents an estimated USD 250 million to USD 400 million opportunity in 2026, expanding rapidly as terminal operators leverage technology to mitigate liability and reduce port P&I insurance premiums.

EXISTING EFFORTS

An AI platform analyses existing CCTV feeds from high-traffic zones. It automatically detects and logs near-miss events (e.g., pedestrian breaching a safe zone around a prime mover, vehicle failing to stop at a crossing). These events populate a safety analytics dashboard, allowing HSE officers to identify trends, hotspots, and at-risk teams, enabling targeted training and process improvements.
Gaps remain for a scalable platform capable of processing numerous camera streams. Computer Vision and AI Startups with proven solutions in industrial or workplace safety can provide solutions in this area. Data Analytics and BI startups that can build powerful safety dashboards on top of a video analytics engine can address this opportunity too.