Fire

AI-powered detection systems have become a major talking point in fire safety and industrial monitoring due to rapid advancements. “Smart detection” using AI video analytics is marketed as promising faster alerts and automated analysis using cameras trained to recognise smoke, flame, or abnormal behaviour. Systems are being deployed across infrastructure, industrial sites, and transport; however, detection technology should be judged by how effectively it improves response, not just by how advanced it appears.

Real-world environments introduce complexities that challenge any single detection method, raising the question: is AI alone enough to improve detection reliability and response times? The answer appears to be no, driving interest in integrating technologies that will truly improve detection. To understand why, it helps to look at both the strengths and limitations of AI-based detection systems.

Where AI cameras work well

To detect a fire, AI-powered video analytics use machine learning models trained on thousands of visual scenarios. Systems analyse camera feeds in real time to identify patterns associated with fires, meaning AI cameras can provide wide-area visual coverage, contextual awareness and remote monitoring capabilities.

In environments with clear visibility and stable lighting, AI systems can detect developing fires earlier than traditional detectors. They’re also incredibly valuable for monitoring large open areas, providing visual confirmation of incidents and supporting situational awareness for operators. But while AI performs well in controlled or visually clear environments, many real-world conditions introduce challenges.

Where AI struggles in real environments

AI detection relies entirely on visual input, meaning performance depends on what the camera could actually see. Several factors can reduce reliability, such as smoke obscuring the camera view; poor or changing lighting conditions; dust, fog, or environmental contamination; and physical obstruction/occlusion. Cameras mean maintenance, requiring regular cleaning, reliable positioning and calibration, and ongoing model training and tuning. AI systems operate on probabilistic detection, calculating the likelihood that something is smoke or flame, rather than measuring the event directly. This can very easily lead to false alarms, missed detections, and delayed alerts when visual evidence is unclear – especially when systems are newer. This is where alternative detection approaches, particularly those based on physical measurement rather than visual interpretation, play an important role.

How physics based detection with fiber optic sensing works

Distributed fiber optic sensing technologies measure physical changes along a fiber optic cable, such as temperature. Using techniques like Distributed Temperature Sensing (DTS), a single fiber can act as a continuous temperature sensor over many kilometres. The system sends light pulses through the fiber and analyses the backscattered signal to determine temperature changes along its entire length. This enables operators to detect abnormal temperature increases, identify the exact location of thermal events, and monitor large assets continuously.

Unlike cameras, the system does not rely on visibility or environmental conditions. Fiber optic sensing operates reliably in smoke-filled environments, complete darkness, and dusty or hazardous industrial areas. The fundamental difference lies in how these technologies detect events.

Deterministic vs probabilistic detection

One of the key differences between AI-based detection and physics-based sensing lies in how each system identifies an event.

AI video analytics operate on a probabilistic model. Machine learning algorithms analyse visual data and estimate the likelihood that what they are seeing represents smoke, flames, or another abnormal condition. The system essentially asks: “How similar is this visual pattern to examples of fire or smoke?”

This approach can be very effective when visual conditions are clear and the scenario closely resembles the data used to train the model. However, because it relies on interpretation, the output is always based on probability rather than direct measurement. Factors such as lighting variation, visual obstructions, dust, or steam can affect how confidently the system classifies an event.

Physics-based detection systems work differently. Technologies such as distributed fiber optic temperature sensing operate on a deterministic principle, meaning they measure physical changes directly rather than interpreting images. A fiber optic cable acts as a continuous temperature sensor along its length, detecting measurable thermal changes and identifying exactly where they occur.

Instead of estimating whether an event might be occurring, deterministic sensing answers a more direct question: “Is there a measurable temperature rise consistent with a developing thermal event?”

Both approaches provide valuable information. Probabilistic systems offer situational awareness and visual context, while deterministic systems provide precise physical measurements and location data. Understanding the distinction helps explain why many safety strategies now combine the two.

The shift toward hybrid detection

Rather than replacing one technology with another, many operators are now moving toward hybrid detection strategies that combine multiple sensing approaches.

In complex environments such as industrial facilities, transport infrastructure, or tunnels, no single detection technology can cover every possible scenario. Visual systems can provide wide-area monitoring and allow operators to see what is happening in real time, while fiber optic sensing can deliver continuous thermal monitoring along critical assets.

By integrating these technologies, operators gain a more complete picture of developing risks. For example, distributed temperature sensing can detect abnormal heat developing along a cable run, conveyor, or tunnel ceiling long before flames or smoke become visible. At the same time, video systems can provide visual confirmation and situational awareness once an alert is triggered.

This layered approach offers several advantages. It helps reduce blind spots where one system might struggle, improves confidence in alarms, and allows operators to respond with more accurate information about what is happening and where.

As infrastructure becomes larger and more complex, the industry is increasingly recognising that smart detection is not about choosing one technology over another but about combining the strengths of different systems.

What actually improves emergency response

Ultimately, the goal of any detection system is not simply to identify an event; it’s to enable faster, more effective responses.

In safety-critical environments, the difference between early warning and delayed detection can be significant. Detecting abnormal heat, friction, or electrical faults at an early stage allows operators to intervene before conditions escalate into a fire, major equipment failure, or operational shutdown.

This is why the most effective detection strategies focus on delivering clear, actionable information. Technologies that provide continuous monitoring and precise location data allow teams to quickly identify where an issue is developing, investigate the cause, and take corrective action. At the same time, visual systems can provide the wider context needed to confirm events and coordinate responses across large or complex facilities.

As detection technologies continue to evolve, the industry is increasingly recognising that “smart” detection is not defined by AI alone. The real value lies in how different technologies work together to deliver faster detection, better insight, and more confident decision-making when it matters most.

For organisations responsible for protecting critical infrastructure, the question is no longer whether to use AI or physical sensing but how to combine the right technologies to strengthen overall detection and response capabilities.

If you’re exploring smarter fire detection strategies for complex environments, learn more about how distributed fiber optic sensing can support earlier detection and faster responses: https://www.bandweaver.com/fiber_optic_sensing_technology/distributed-temperature-sensing/

Fire detection is no longer just about triggering an alarm, it’s about interpreting heat data correctly in complex, high-risk environments. As critical infrastructure operates under greater thermal stress, traditional detection methods struggle to balance early warning with false alarm reduction.

Modern fiber optic fire detection systems take a different approach. By continuously measuring temperature along every metre of an asset and applying intelligent algorithms to that data, they can detect fire conditions earlier, more accurately, and with far greater reliability. Understanding how these algorithms work and how detection thresholds are set is now essential for achieving effective fire protection.

This blog post explores how smart algorithms improve fire detection accuracy, why legacy systems fall short, and how intelligent threshold design enables faster, more dependable responses in demanding environments.

Why fire detection accuracy depends on algorithms

Fire detection is effectively a data interpretation challenge, not just a sensing one. Modern environments generate constant background heat from machinery, power systems, vehicles and processes, so a simple ‘trigger point’ detection system can’t distinguish between normal operational heat and early-stage fire conditions. Accurate detection depends on understanding how quickly, where, and in what pattern temperature changes occur.

Poor algorithm design leads either to nuisance alarms or dangerously delayed detection, which means as assets become larger, hotter, and more complex, algorithm intelligence becomes critical.

The role of fiber optic distributed temperature sensing (DTS)

Distributed temperature sensing systems measure temperature continuously along the full length of a fiber optic cable, so unlike point detectors, DTS provides a complete thermal profile of the protected asset. Temperature is measured at high spatial resolution (typically every 1 metre), with frequent sampling allowing systems to observe temperature trends, not just isolated spikes. This continuous coverage ensures no blind spots in long or complex environments. Our distributed temperature sensing systems collect and provide all the raw data needed for advanced algorithmic analysis.

Key algorithmic approaches used in fiber optic fire detection

Absolute temperature thresholds

These trigger alarms when the temperature exceeds a predefined value and are simple and reliable for clearly defined fire scenarios. However, the algorithm can be slow to respond if thresholds are set too high and has an increased risk of false alarms if thresholds are set too low.

Adaptive Rate-of-rise detection

The Adaptive Rate-of-Rise Alarm detects abnormal rates of temperature increase by comparing current conditions to an adaptive thermal baseline. Unlike conventional fixed thresholds, it responds rapidly to emerging fire behaviour while remaining immune to gradual ambient and environmental temperature changes.

Hot Spot Detection

The Hotspot Detection Alarm identifies localised abnormal heating along the sensing fiber by comparing each point to surrounding conditions and calculating the deviation from the average within the zone. It is highly effective at detecting early-stage thermal events caused by friction, electrical loading, or mechanical issues, enabling intervention before escalation occurs.

Combined algorithm logic

Many modern systems have found the ultimate solution by applying multiple algorithms simultaneously. This enables cross-verification across algorithms to improve detection confidence and provides faster alarms without sacrificing reliability; if anything, it increases it.

Why tuning matters when choosing fire detection thresholds

Threshold selection has a direct effect on both detection speed and false alarm rates. Often, generic thresholds rarely suit complex or high-risk environments, so operational temperatures must be fully understood before thresholds can be set. Even just marginally different zones in one site can require different detection logic.

This is why using an intelligent, adaptable fire detection system is so vital, as intelligent systems allow thresholds to evolve as operating conditions change. It’s a system designed to work for each unique application – as opposed to poorly tuned systems which erode trust and reduce operational effectiveness.

Why legacy fire detection systems fall short

Many of the widely used legacy systems rely on single-point or single-condition triggers, where limited contextual awareness is their downfall, leading to delayed or missed alarms. The very popular smoke-based systems depend on air movement and fire development, meaning no possibility of early detection. Alternatively, heat cable systems often lack spatial resolution and algorithm flexibility because fixed logic cannot adapt to changing asset behaviour. This means, in high-interference environments in particular, legacy systems struggle to keep up with the changing surroundings.

How Bandweaver applies ‘Smart Algorithms’ in FireLaser and T-Laser

Our FireLaser and T-Laser systems use high-resolution DTS combined with intelligent alarm processing to support multiple alarm types, including absolute temperature, adaptative rate-of-rise, and hot spot detection. Detection logic can be configured per zone to reflect asset risk and behaviour, allowing for a more adaptable fire detection strategy suited to each environment. High sampling frequencies enable early recognition of abnormal thermal trends with a software-driven configuration to allow optimisation without physical systems changes. Both systems are designed for demanding environments such as tunnels, conveyors, power plants, and other industrial sites.

Intelligent algorithms can rapidly distinguish between real fire signatures and benign thermal events through localised analysis to prevent system-wide alarms from normal temperature shifts. Early detection occurs closer to the ignition source, allowing the fire to be stopped at the earliest moment before it can damage assets or infrastructure. Due to its smart detection abilities, the system results in significantly fewer false alarms, improving operator confidence and response quality whilst lowering operational disruption and maintenance costs.

As mentioned, faster detection limits damage and reduce recovery time, whilst precise location data enables targeted response and intervention. However, the passive fiber optic sensing cable also reduces maintenance requirements and exceeds the typical lifespan of a detection system to deliver a lower total cost of ownership. It’s this improved reliability all round that supports regulatory compliance and insurance confidence.

Smarter systems lead to safer outcomes

High-performance fire detection is no longer defined by sensitivity alone but by intelligence. In complex, high-risk environments, smart algorithms are essential to transform raw temperature data into clear, actionable insight. When detection thresholds are intelligently designed and tuned, operators gain earlier warnings, greater confidence, and the time needed to intervene before incidents escalate.

As infrastructure grows more demanding and risks continue to evolve, fire detection systems must keep pace. Protecting the assets that support people, communities, and essential services requires solutions built for modern conditions, not legacy limitations. Smart, software-driven fire detection is the future, and the organisations that prioritise it today will be the ones best prepared for tomorrow.

If you’re reviewing your fire detection strategy, now is the time to ask whether your system is truly working for you. Explore how intelligent, fiber-optic fire detection can deliver earlier insight, greater reliability, and long-term confidence: https://www.bandweaver.com/fiber_optic_sensing_technology/distributed-temperature-sensing/

Major, high-profile car park fires and tunnel incidents in the past have demonstrated the same basic failure: by the time anyone knew what was happening, it was already too late.

In the UK two cases tell this story and show the real cost of poor detection. The Liverpool Kings Dock fire of 2017 and the Luton Airport Terminal Car Park fire of 2023. A report from the Merseyside fire brigade showed the Liverpool fire destroyed 1,150 vehicles and caused severe structural damage, a clear indication of how fast modern vehicle fires can escalate. Meanwhile, the Luton Airport fire had a major operational impact, affecting 1,300 vehicles and causing disruption for months.

These incidents have exposed a consistent problem: legacy standards, systems designed for a bygone era, and a culture that treats detection as a checkbox rather than a life-saving tool.

The pattern we keep repeating

There is a substantial gap between laboratory or design assumptions and real-world conditions. This means detection systems that have been specified for decades aren’t fit for purpose when faced with modern cars, tighter spacing and plastic or fuels that burn hotter for longer. Research[1] from 1968 simply isn’t  enough to keep pace with current vehicles and environments.

This leads to significant problems such as late or manual detection. Interestingly, in both cases, the initial alert didn’t come from the fire detection systems but was raised by a member of the public who saw the flames and called from a mobile phone. Response teams also suffer from a lack of precise location data; in the Liverpool fire, the response team was sent out to fight the fire on the wrong level of the car park, completely missing the growing threat on other levels. A lack of targeted response increases danger to civilians and first responders as well as increasing the damage to vehicles, infrastructure and assets. To make matters worse, systems are often installed and forgotten about, which is a serious issue when dealing with systems that require adequate and continuous maintenance and realistic testing to operate successfully.

By contrast, some countries, including the Netherlands, Norway, and  Turkey, enforce stricter commissioning and are more accepting of distributed sensing solutions. When regional regulation changes are made, they drive adoption, and the market uptake increases. So, the route to improved fire detection is simpler than it seems.

The cost of complacency

The fire at the Liverpool Kings Dock in 2017 is the perfect example of how a small fire can rapidly escalate if not dealt with properly. Initially the fire was confined to a single car, but undetected, it was allowed to spread rapidly, causing structural damage and catastrophic asset loss. Observations from engineers after the fire noted that extreme heat caused spalling and structural failure, which shows that modern car fires behave more like petrochemical events in some cases.

The Luton Airport car park fire was a similar incident. From a small vehicle fire, the blaze spread, leading to a partial collapse of the car park, major flight disruption and multiple firefighters treated for smoke inhalation. The Bedfordshire Fire Brigade published a report concluding that the absence of sprinklers contributed to the extent of the blaze and recommended the consideration of mandatory suppression for car parks.

Both these events show that when detection and suppression lag behind modern fire dynamics, human life is put at risk, financial and operational costs soar, and the fire response is hindered.

What other countries are getting right

In the Netherlands they have strict commissioning standards, such as pan-fire testing, to set a high bar for what a detection system must detect in car parks. Our fiber optic linear heat detection systems are among the few solutions that can pass these tests.

Norway makes consistent regulatory updates for tunnels and infrastructure and are creating new demand for systems to meet these regulations. This goes to show that regulatory change is a powerful adoption driver.

In clusters across the globe, peer adoption drives improved fire detection systems. Successful installation and operation in one country or state often leads competitor sites to follow, creating clusters of development and regulatory improvement.

Where regulators or clients demand performance-based testing instead of checkbox compliance, the uptake of contemporary solutions like fiber optic linear heat detection increases.

How modern technology could have altered outcomes

Simply put, fiber optic LHD uses continuous distributed sensing along every metre of fiber, not single-point sensors. Rate of rise and deviation alarms detect thermal anomalies early, often before visible flame or significant smoke. The system allows pinpoint location targeting; to within 1 metre, enabling targeted response to reduce search time.

If we apply the LHD system capabilities to the two incidents, it leaves us with a very different result. In either scenario a distributed LHD system installed throughout the car park with sensors spaced appropriately to the volume of cars would be able to detect the initial blaze within seconds of it spiking in temperature. This would raise an alarm, alerting operators with the exact location and triggering automated responses. Early intervention would stop the blaze in its tracks, preventing hundreds of thousands of pounds of damage and disruption to business. Each system pays for itself over and over by averting each incident.

LHD is even stronger when integrated with suppression, ventilation control, SCADA and CCTV, the system becomes an active control loop from detection to isolation and suppression.

Why does change still lag?

A more conservative consulting culture has a huge impact on regulatory change. Many consultants and standards bodies err on the side of caution, adopting new technology slowly and requiring extensive proof before approving new sensor types for certain applications. Despite fiber optic LHD having been used since the 1980s, many still regard it as new because they haven’t revisited it in years.

Standards are often deliberately set more conservatively for safety and are then slow to move. Significant incidents often prompt regulatory change, but this is reactive rather than proactive. It’s time for those in the industry and standards bodies to take Bedfordshire Fire’s recommendations: mandating automatic suppression for open multi-storey car parks and stronger detection systems.

Cost can be a barrier to fiber optic LHD, as the systems are seen to have a higher upfront cost than other point sensors. But the cost per metre and long-term reliability make it cost-effective at scale, and the technology is more cost effective for operators over time. The lifecycle and complexity of these systems are also misunderstood, but the reality is completely opposite, with their long lifecycle and lack of complex maintenance being a key benefit.

From reaction to prevention

Repeated fire safety failures show a pattern: that modern risks demand modern distributed detection and integration, not legacy point sensors and box-ticking compliance.

It’s time for asset owners, consultants and regulators to reassess risk with the current operating realities of different environments and prioritise performance-based procurement with strict commissioning standards. Consider fiber optic detection as a core element of a layered fire strategy; it’s proven itself in numerous projects like the Turin Metro and parking applications to provide earlier alerts and precise location information.

Learning from these failures is no longer optional; it’s vital to prevent future catastrophes. Our partners are part of this change, bringing effective, safer fire detection solutions to industries across the globe. If you’re ready to make a difference, here’s how: Join our global network of partners.

[1] , The Ministry of Technology and Fire Offices’ Committee Joint Fire Research Organisation produced Fire Note No.10, “Fire and Car-Park Buildings”