the physical layer
a cutting edge industry newsletter for security integrators
Release 1 | 2025 Q2
by Timur Gabaidulin
[email protected]
Welcome to The Physical Layer, where the digital future meets physical security. In this inaugural issue, we explore how AI is reshaping fire detection and bring you actionable insights that can transform your business today.
Don't Leave Money on the Table, AI is the Next Security Gold Rush
As artificial intelligence continues to advance, computer vision is quickly becoming a powerful tool for critical safety applications like fire and smoke detection. Adoption is already accelerating in high-risk environments such as oil refineries, steel mills, and manufacturing plants, where flammable materials are part of everyday operations. As AI models become more efficient and require less computing power, this technology is expected to expand into retail, commercial, and other lower-risk spaces as well.
Traditional fire detection systems rely on sensors that trigger only after smoke or heat reaches a certain threshold. Computer vision takes a different approach. By analyzing live video feeds in real time, it can spot early visual indicators like flickering flames or drifting smoke before those physical cues become detectable. This early warning enables safety teams to act faster, potentially containing incidents before they escalate.
One particularly novel and interesting application of CV (computer vision) models in CCTV is the work being done by FireSafe AI, an Edmonton-based Canadian startup focused on wildfire prevention and response. Their system uses solar-powered, mobile surveillance units paired with drones and AI to monitor for smoke and ignition events in real time, even in remote areas thanks to 4G/5G and Starlink connectivity. The AI engine continuously learns from local environmental data, improving detection accuracy over time. FireSafe's platform is hardware-agnostic, integrating easily with existing CCTV infrastructure, and is already being used by municipalities and critical industries to proactively manage wildfire risk. It's a practical example of how intelligent video analytics can extend the value of traditional camera systems far beyond passive monitoring.
Despite growing demand for these AI-driven systems, many small and mid-sized security integrators, and even some larger ones, are missing out on the opportunity. The issue isn't demand or hardware. It's a skills gap. Most security techs aren't trained in the software side of things, such as programming languages like Python, AI frameworks, or tools used to deploy machine learning models.
As a result, when clients request AI-based solutions, integrators often have to bring in IT consultants or software firms. That drives up costs and pushes integrators to the sidelines. Instead of owning the solution, they simply limit themselves to installing hardware. But it doesn't have to be that way. With a modest investment in training and the use of open-source platforms YOLOv8 (You Only Look Once), integrators could deliver complete AI-powered systems themselves and capture more value in this growing field.
Setting up a pre-trained model is often straightforward. In many cases, it's no more complicated than installing a server in a rack, connecting it to a UPS, plugging it into the existing security or production network, running a site-acceptance test, and launching it into production. A single GPU server equipped with something like an NVIDIA T4 or RTX 3090 can process up to 30 high-resolution video feeds in real time for fire or smoke detection. These units typically cost around $5,000 to $5,500, allowing integrators to offer turnkey AI-powered detection systems at a fraction of what IT consulting firms might charge, unlocking new revenue streams without burdening clients with enterprise-grade pricing.
Recent fire detection implementations show impressive results. Modified YOLOv8 models achieve 97.1% precision overall, with smoke detection at 99.3% and fire detection at 95.7%. Some optimized versions reach 98% accuracy with 100% precision and 99% recall (true positives + false negatives). For context, traditional smoke detectors have false alarm rates of 5-15%, making these AI accuracy levels transformative for reducing nuisance alarms.
YOLOv8 nano can achieve 525 FPS on optimized CPU deployment, while the small version reaches 209 FPS. For typical security camera feeds at 30 FPS, this means real-time processing with significant headroom. Even on modest hardware, inference times of 10-35ms are achievable, crucial for early fire detection where seconds matter.
GPU acceleration provides 10-50x speed improvements over CPU-only processing, but isn't always necessary. For installations with existing network video recorders, edge processing on modern IP cameras can handle YOLOv8 nano models without additional hardware investment.
A fire detection system processing 16 camera feeds simultaneously would need roughly 480 FPS capacity total. With optimized deployment, a single mid-range server can handle this load while maintaining sub-second detection times, fast enough to trigger suppression systems before human operators even notice the alert.
To build a computer vision system that can automatically detect fire from images or video feeds, the development process generally follows three main phases: preparation, training, and deployment.
Phase 1: Preparing the Data
Like any real-world learning process, it starts with gathering quality study material. In this case, that means collecting a large set of images, some with fire, some without. These images must be labeled so the AI understands what it's looking at. Labeling can range from tagging an entire image as "fire" or "no fire" to outlining exactly where fire appears within the frame. Once labeled, the dataset is split into training and testing sets. The training set teaches the model, while the testing set (kept separate) measures how well the model performs on new, unseen examples. This is very similar to studying with flashcards, then taking an unfamiliar test to prove you understand the material.
Phase 2: Training and Evaluation
With the data in place, the next step is selecting an appropriate computer vision model; the brain of the system. The model is trained by feeding it the labeled images from the training set. Initially, its predictions are mostly wrong. But after thousands of corrections, it begins to pick up on patterns: the shape of flames, the color of smoke, the way fire spreads. As it trains, the model adjusts its internal parameters to improve accuracy. Afterward, it's evaluated using the testing set to see how well it generalizes. Does it correctly detect fire? Does it produce false alarms? This phase ensures the system is ready for real-world deployment.
Phase 3: Deployment and Integration
Once trained and validated, the model is ready to be deployed. This usually mean running it on a server, or in some cases, an edge device like NVIDIA Jetson Orin Nano. It's integrated into a broader system that feeds it live video or images. Based on its training, it continuously outputs a prediction: "fire" or "no fire." If fire is detected, the system can alert personnel or trigger an automated event via GPIO. But deployment isn't the final step. Real-world environments are complex, and conditions change. That's why ongoing maintenance is essential, monitoring performance, updating the model with new data, and retraining as needed to maintain accuracy.
Putting it all together...
Building a fire detection model is a structured process that begins with labeled data, moves through training and evaluation, and ends with real-time deployment. For integration professionals, understanding this workflow opens the door to delivering powerful, AI-enhanced fire detection solutions that go well beyond traditional sensors.
Of course, knowing how these systems work is only part of the equation. The real question for many integrators is, who's going to build them?
Designing and deploying custom fire detection models, or any vision-based AI system, requires a skill set that extends beyond traditional CCTV installation and maintenance. This isn't just another firmware upgrade. While there are plenty of (costly) off-the-shelf solutions that cover the basics, tailoring models to specific sites or risk profiles demands technicians who understand both the hardware and the software. That's where training becomes essential.
| Area | Tools & Resources |
|---|---|
| Python basics | Python Crash Course, Automate the Boring Stuff |
| YOLOv8 | Ultralytics Docs, YouTube Tutorials |
| Labeling tools | Roboflow, LabelImg |
| Model training/testing | Google Colab (for free GPU), local training on NVIDIA GPU server |
| Integration | Manufacturer documentation such as Milestone MIP SDK docs, FastAPI guides |
Beyond hardware, the real long-term value lies in human resources. When integration companies invest in upskilling their technicians, teaching them how to use tools like YOLOv8, manage datasets, and deploy AI models, they're future-proofing their workforce. For employees, this opens doors to career growth and higher earning potential, transforming what was once a purely hands-on trade into a hybrid role that combines traditional integration with machine learning. Clients benefit from smarter, more responsive systems at lower costs, backed by professionals who already understand their infrastructure. And for companies, it means retaining talent, improving margins, and staying competitive in a rapidly converging field where IT and physical security are increasingly intertwined.
Artificial intelligence is reshaping the life safety landscape, and integrators who adopt these tools early will be best positioned to lead. With accessible training programs and the rise of user-friendly, open-source platforms, the real barrier to entry is no longer technical, but educational. By investing in their technical teams, firms can enable technicians to move beyond traditional roles and take ownership of high-value, AI-driven solutions. This shift not only drives business growth but also cultivates a more agile, future-ready workforce prepared to thrive in a rapidly evolving industry.
Q2 News
Hikvision has launched its new Guanlan Large-Scale AI Models
New AI models power smarter cameras and NVRs with better detection, natural language search, and reduced false alarms.
Hanwha Vision has unveiled the 2nd generation of its P and X series cameras
Next-gen cameras get clearer images, smarter AI detection, and real-time privacy masking with the new Wisenet 9 chip.
Genetec has introduced new advanced mapping features to its Cloudrunner vehicle-centric investigative system
New map features help law enforcement track vehicles and camera coverage more visually and efficiently.
LenelS2 has officially released NetBox version 6.2
Update adds better video, mobile badge support, and expanded hardware compatibility for access control systems.
LenelS2 has announced the full commercial availability of its BlueDiamond NFC mobile credential solution featuring corporate badge in Google Wallet
Employees can now access secure areas with Android phones using Google Wallet—no physical badge needed.
Milestone Systems has released XProtect 2025 R1
New release boosts cloud video integration and adds advanced vehicle ID features like make, color, and movement tracking.
Digital Monitoring Products (DMP) has introduced JamAlert™
Device detects cellular jamming and alerts security systems in real time to prevent signal-blocked break-ins.
Axis Communications announces two AI-powered PTZ cameras
The AXIS Q6355-LE (1080p) and Q6358-LE (4K UHD) PTZ cameras feature ultra-light-sensitive ½" sensors, Lightfinder, Forensic WDR, OptimizedIR (300 m), 31x optical zoom, fast-tracking, audio support via midspan, and four I/O ports for reliable performance in all lighting conditions.
New app development environment enables custom, edge-based applications directly on controllers
At ISC West 2025, HID Global showcased the new Mercury embedded application environment, a powerful open platform developed by its subsidiary Mercury Security. Built to run custom partner and OEM apps directly on Mercury MP Controllers, the platform shifts intelligence to the edge.
About the project and the curator
Thank you for reading the inaugural release of The Physical Layer.
I'm Timur (or Tim), and I have over a decade of experience in the security integration field. I've worked with industry leaders such as Siemens and Johnson Controls, serving clients across the Canadian market. Originally from Toronto and now based in Ottawa, Ontario, my background covers the full range of our industry. Access control, intrusion detection, surveillance systems, and the infrastructure that connects them all.
The Physical Layer was born from a simple observation; unlike the AI or software world, our industry lacks insightful, fast moving newsletters that keep integrators informed and ahead of the curve. While other sectors have embraced dynamic, insight driven newsletters, physical/electronic security has largely stuck to dry product announcements and vendor press releases.
This newsletter changes that. Each quarter, I will try to deliver the strategic insights, emerging trends, and practical knowledge that help security integrators stay ahead of the curve. From AI implementation guides to market analysis, I will focus on what matters most, which is helping you grow your business and expand your capabilities.
The Physical Layer will always be free, because knowledge should never come with a paywall. That said, creating and curating this content does take a significant amount of time, effort, and a few out of pocket costs. If you find value in what you're reading and want to help keep it going, consider supporting this project via Ko-Fi using the link below: