This research introduces AH-YOLO, a lightweight and highly accurate deep learning model optimized with infrared thermal imaging for real-time fire detection in aircraft hangars, outperforming existing methods in both speed and precision.
When it comes to aircraft hangars 🛩️, even the smallest fire 🔥 can lead to massive damage and potential disasters. Traditional smoke detectors or temperature sensors often react too late—or not at all. But thanks to a powerful new AI model called AH-YOLO, we now have a fast, accurate, and lightweight solution for real-time fire detection using infrared vision.
Let’s break down what AH-YOLO is, why it matters, and how it’s changing fire safety forever.
Imagine a giant hangar filled with planes, fuel, and composite materials. One small spark 🔥 could turn the whole space into an inferno within minutes. Current fire safety systems often use:
While these work decently in homes or offices, they’re just not fast or smart enough for complex places like hangars. Why?
👉 Big open spaces = uneven smoke spread
👉 Low-light conditions = tricky for cameras
👉 Fires may be small or hidden behind smoke
So what’s the fix? 💡 Artificial Intelligence + Infrared Imaging.
The researchers behind this study designed AH-YOLO, an improved and lightweight version of the famous YOLOv8 model (You Only Look Once) that detects fires using infrared thermal images. That means it “sees” heat, not just flames or smoke. 🔥🌈
It’s not just another fire detector. AH-YOLO is built for speed, accuracy, and efficiency using three smart tools:
To train AH-YOLO properly, the researchers didn’t rely on simulations—they lit actual fires (safely!) in a real aircraft hangar in China. 🚒✈️
🔥 Materials used for burning:
Using a dual-lens infrared thermal camera, they recorded flame behavior under different conditions:
✅ Different distances: 10m, 30m, 50m
✅ Different heights: 1m, 3m, 5m
✅ Different flame sizes and intensities
In total, they created a custom dataset of 1500 infrared images, including both fire and non-fire situations. This made sure the AI model learned what real fire looks like—and what it doesn’t.
Let’s geek out a little. Here’s what powers AH-YOLO under the hood:
A modular neural network that cuts down on heavy computation. It's fast, efficient, and perfect for edge devices (like drones or security cameras). Think of it as the model’s super-slim engine 🏎️.
This acts like the AI’s attention span. It tells the model where to “look” in the image—focusing on the hottest spots or flame patterns 🔍🔥.
Enhances the model's ability to handle scale and location variation. Fires don’t always look the same—some are far, some small. DyHead helps adapt to these changes 🧠.
These modules were perfectly placed into YOLOv8’s architecture to maximize performance while keeping it light.
So how did AH-YOLO perform?
| Metric | YOLOv8n (original) | AH-YOLO (new model) |
| [email protected] (accuracy) | 90.2% | 🚀 93.8% |
| Parameters | 3.01M | 2.54M ✅ |
| FPS (Frames/sec) | 142 | 171 ⚡ |
It’s:
💥 That’s a serious upgrade!
Compared to other models like YOLOv5n, EfficientNetV2, and MobileNetV3, AH-YOLO outperforms them in both speed and accuracy—making it ideal for real-time applications in hangars.
AH-YOLO isn’t just another research paper—it’s a practical, deployable solution for enhancing fire safety in critical infrastructure like:
✈️ Airports and aircraft hangars
🏭 Industrial plants
🛳️ Ships and cargo bays
🏢 Large public buildings
By combining infrared imaging with AI, it works even in low light or smoky conditions—places where traditional detectors fail. 🕳️🔥
And since it’s lightweight, it doesn’t need powerful GPUs to run—making it perfect for edge computing 🌐💻.
While AH-YOLO is already a game-changer, there’s more to come:
Thanks to AH-YOLO, we’re a step closer to safer skies and smarter buildings. It shows how engineering and AI can work hand-in-hand to solve real-world problems—literally saving lives and infrastructure 🔧❤️🔥.
🔥 Fire Detection - The tech used to sense and spot fire early. Think of it as a smart “sniffer” that knows when something is burning—even before your smoke alarm goes off.
🛩️ Aircraft Hangar - A large building where airplanes are stored or repaired. It’s like a garage for planes—but way bigger and more fire-prone due to fuel and materials inside.
🎥 Infrared Thermal Imaging - A camera technique that sees heat instead of light. Hot objects like flames glow brightly—even in total darkness or thick smoke. 🌡️🔥 - More about this concept in the article "Beating the Heat: How Cool Roof Coatings Can Save Cities from the Urban Heat Island Effect 🌆 ☀️".
🤖 YOLO (You Only Look Once) - A fast deep learning model that finds objects in images. It’s like giving your camera instant object recognition superpowers—detecting things in a single glance. 👀⚡ - More about this concept in the article "Smart Bees, Smarter Tech 🐝 How Deep Learning is Changing Hive Monitoring Forever!".
🧠 AH-YOLO - A smarter, faster version of YOLO, made for fire detection using infrared. It’s specially designed to spot fires in tricky places like hangars—accurate, quick, and lightweight.
🧩 CNN (Convolutional Neural Network) - A type of AI that learns to understand images. It’s how your phone knows your face—and how AH-YOLO knows fire. 🧠📷 - More about this concept in the article "Quantum-Inspired Algorithm Tackles Urban Noise Pollution: A Breakthrough for Smart Cities 🌆 🎤 🔊".
💡 MobileOne - A lightweight building block for neural networks. It helps AI run faster and smoother—great for mobile or real-time use.
🎯 CBAM (Convolutional Block Attention Module) - A tool that tells the AI where to focus in the image. It’s like a highlighter for important spots, helping the model “pay attention” to flames.
🧮 DyHead (Dynamic Head) - An AI module that adapts to fire size, shape, and distance. Flames come in all forms—DyHead helps the model adjust to each one.
🎯 mAP (Mean Average Precision) - A score that shows how good the model is at detecting things. The closer to 1.0 (or 100%), the better. Think of it as the model’s report card. 📊✔️ - More about this concept in the article "Smart Drones for Tiny Creatures: How AI is Revolutionizing Insect Monitoring 🚁 🦋".
⚡ FPS (Frames Per Second) - How many images the model can analyze each second. Higher FPS = faster detection = safer buildings. 🚨🏃♂️ - More about this concept in the article "Revolutionizing Drone Detection: The RTSOD-YOLO Breakthrough 🚀".
Source: Deng, L.; Wang, Z.; Liu, Q. AH-YOLO: An Improved YOLOv8-Based Lightweight Model for Fire Detection in Aircraft Hangars. Fire 2025, 8, 199. https://doi.org/10.3390/fire8050199
From: Civil Aviation Flight University of China; Sichuan All-Electric Aviation Aircraft Key Technology Engineering Research Center.
