Imagine walking into a plant bay where trucks, forklifts, and workers move constantly. The chances of an accident are pretty high: a blind corner, an obstructed pathway, or a crate falling from a top rack. Since accidents like these are common, embracing computer vision-based surveillance can help prevent such incidents before they become injuries or worse.
In this blog, you’ll discover:
Why nearmisses matter more than we often admit
How vision systems can detect and reduce them
Lessons from global examples in FMCG and logistics
Why Near Misses Need Attention
Near misses are frequent in high-traffic plant bays. The Health and Safety Executive mentioned 124 work-related deaths in 2024-2025. While fatality incidents often get registered, most nearmisses go unrecorded, unexamined. That means the root causes, such as layout flaws, blind spots, and rush during shift change, remain hidden.
One firm in Europe’s shipping and logistics sector saw a 415% increase in near-miss visibility after deploying AI-based monitoring. High traffic environments breed risk and need our immediate attention:
Vehicles reversing or turning in narrow corners
Workers walking through routes used by forklifts
Changing shifts and increasing traffic volume
Poor lighting or obstructions reducing visibility
How Visual Surveillance Helps
Visual surveillance enables intelligent risk management, driving proactive safety and operational excellence. These cutting-edge systems continuously examine live video feeds from busy plant locations, spotting and marking possible security breaches, operational inefficiencies, and safety risks.
Toyota’s AI-driven computer vision system monitors assembly lines for unsafe worker behavior in real time.
Siemens employs an AI-powered digital twin solution that minimizes incidents and improves emergency response effectiveness.
DHL uses computer vision technology solutions to track PPE non-compliance among the workforce and identify the root cause, such as discomfort or the wrong size.
Vision-based cameras and intelligent analytics catch issues that the human eye might miss; this includes:
Real-time detection. Vision systems watch zones where vehicles and people intersect. For instance, the system might alarm when a forklift approaches a pedestrian zone.
Behavior patterns. Vision cameras track movement over time. Supervisors receive data that surfaces root causes if workers habitually approach moving machinery. They can then warn workers and prevent an untoward incident from happening.
Automated logging and analysis: Vision systems flag and log near misses with timestamps, images, and location. Safety managers can analyze trends instead of depending on their memory or manual tallying.
SOP adherence: Vision AI surveillance ensures compliance with standard operating procedures. Doing this lets businesses improve their workflows and identify areas for improvement.
Restricted area monitoring: With a modern AI system, warehouses can monitor access to restricted areas and alert managers of unauthorized entry.
Embarking on the Vision AI Surveillance Journey
Streamlining your supply chain and logistics operations with intelligent automation is no longer a question of when, but how! With today's modern vision AI solutions, warehouse operations can be efficiently organized, package sorting can be automated, and accurate inventory tracking can be ensured. In addition, they predict potential disruptions, thus ensuring smooth flow and efficient operations across the supply chain.
You need not try to retrofit the entire site overnight just to get started. Advanced vision platforms can work with your existing camera infrastructure. They can issue alerts via dashboards, lights, and even mobile notifications. These modern systems can detect missing helmets, dangerous proximity, and unauthorized zone breaches. Using them helps you move from reactive fixes to proactive safety design.
To enable vision AI surveillance, start small. Focus on:
High-risk zones: Focus on truck bays, forklift intersections, and blind corners.
Simple metrics: Track near-miss frequency by time of day, shift, or location.
Iterative changes: Adjust Lane markings, add mirrors, shift traffic patterns; measure results.
Cultural integration: Share video-based lessons in safety training sessions and allow operators to contribute to possible solutions.
Final thought
High-traffic plant bays demand more than cameras. They need active visibility into theon-ground reality. Tracking near-miss accidents in real time can improve the overall safety and efficiency of plant operations. Over time, overall throughput rises, and employees feel safer.
Vision-based surveillance improves workflows, finds areas for improvement, and guarantees adherence to standard operating procedures. By implementing the appropriate methods, you can ensure a secure workplace for staff.
FAQs
Why is it critical to track near-misses in high-traffic plant bays?
Tracking near-misses helps prevent accidents before they impact operations or endanger worker safety.
How does vision AI surveillance reduce near-miss incidents?
Vision AI systems process real-time video feeds to detect and flag risks such as pedestrians entering forklift zones, obstructed walkways, or unauthorized access. This allows safety teams to intervene before an accident occurs.
Can Vision AI work with existing security infrastructure?
Yes. Most modern vision AI surveillance platforms integrate seamlessly with current infrastructure, minimizing setup time and cost while unlocking powerful real-time safety insights.