People drifting under suspended loads during lifts is one of the most common—and preventable—risks in manufacturing. AI video analytics can detect when someone enters the exclusion zone during active lifting and alert supervisors in real time. Start with one high-traffic lifting bay, enforce a simple rule, and fix the patterns that keep pulling people into the danger zone.
How To Stop People From Walking Under Suspended Loads
If you are looking up preventing line of fire incidents in manufacturing with ai, you are likely trying to solve a very specific problem: people drifting into the danger zone during lifting, especially when a load is suspended and moving across a bay.
This article focuses on one high-impact risk that factories can control quickly: keeping unauthorised people out of suspended-load areas. You will get a simple way to define the danger zone, reduce alert noise, and build a repeatable response that supervisors will actually use.
Before you plan anything, it can help to look through Invigilo’s solutions and use cases so you can match the right workflows to your plant’s lifting areas.
A line of fire incident happens when a person is in the path of something that can strike, crush, or trap them. In manufacturing, that “something” often involves moving loads, moving equipment, or sudden movement during routine work.
This guide focuses on the most direct and common version: people under, inside, or too close to a suspended load while it is being lifted or moved.
During lifting, the line of fire is not only “under the hook”. It usually includes:
If someone is in these areas during a lift and they are not part of the authorised lifting team, you have a preventable exposure.
Most factories already have lifting rules. The gap is not knowledge. The gap is real-time control.
Line of fire exposure still happens because:
This is where AI safety cameras can help. Not to replace lift planning, but to make the drift into danger visible the moment it starts. If you are considering camera-based line of fire detection, you can check Invigilo’s suspended-load workflow and see whether it fits your lifting areas.
The highest-risk situations are usually the ones that happen often. Repetition is what makes prevention measurable.
Risk clusters around routine moves like:
These tasks feel normal, so people become less careful. That is why they are a good place to start.
Blind spots are not only about camera angles. They are often created by how people move:
A simple rule helps: if people repeatedly end up in the same place during lifts, your layout and routine are inviting it. Controls should remove the invitation, not just remind people to “be careful”.
AI video analytics can monitor a lifting area and alert when someone is inside the defined danger zone while a lift is active. The goal is to stop the unsafe moment before it becomes an incident.
To be useful on a factory floor, detection should be clear and easy to explain:
You are not trying to build a complex system. You are trying to enforce one high-risk boundary consistently.
This is where many programmes fail. If alerts trigger on the people doing the lift properly, supervisors lose trust fast.
A practical setup separates:
When you evaluate Invigilo, this is one of the most important questions to ask: how the workflow is set up to keep alerts meaningful so teams do not tune them out.
If you are unsure whether your lifting bay cameras are suitable, you can share a couple of typical views with Invigilo and ask what is realistic for your layout. That quick check can save weeks of guesswork.
If alerts run all day in a busy bay, people will stop paying attention.
Keep it simple. Define when the rule matters most, for example:
Clear “lift active” windows reduce noise and make the rule feel fair.
AI helps, but the foundation is still a visible, reasonable rule that fits how people work.
Use a rule that anyone can understand in one sentence:
When a load is suspended, only the authorised lifting crew is allowed inside the marked exclusion zone. Everyone else stays out.
Make the zone tight and realistic. If the zone blocks normal movement all the time, people will ignore it. If it matches the real hazard boundary, compliance improves because it feels reasonable.
Alerts only help if the response is clear.
A simple response loop:
This keeps the programme focused on prevention, not blame.
Repeated alerts are useful. They tell you where the system is failing, not where people are “bad”.
Common fixes include:
If lifting is only one part of your risk picture, you might also find Invigilo’s manufacturing guides helpful, especially their posts on PPE compliance monitoring, unsafe proximity to machinery, and forklift safety. For this article, we stay focused on suspended loads.
Think of this section as a checklist of what to look for, using Invigilo as a reference.
For this use case, you want to evaluate whether the workflow supports:
If you want to see what else Invigilo can support in manufacturing beyond lifting bays, take a quick look at their solutions overview and shortlist the workflows that match your plant.
The fastest way to get a useful answer is to share:
You are not trying to test everything. You are trying to prove one high-risk rule can be enforced consistently without creating noise.
If you want fewer line of fire moments during lifting, start small and stay consistent:
If you want to set this up properly and avoid guesswork, contact Invigilo and ask for a line of fire detection walkthrough for your lifting bays. Share your lifting hotspots, a couple of camera views, and how your lifts typically run. Their team can recommend the most suitable workflow for your site and help you scope what is realistic to implement first.
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Ready to elevate safety in your operations? Let’s talk!
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