AH-YOLO: An Improved YOLOv8-Based Lightweight Model for Fire Detection in Aircraft Hangars
Abstract
1. Introduction
- YOLO-based aircraft hangar fire safety detection: this paper demonstrates the use of YOLO for real-time fire detection in aircraft hangars, addressing challenges specific to these high-risk environments.
- Lightweight, high-performance detection models: the AH-YOLO model incorporates MobileOne, CBAM, and a DyHead, improving both the accuracy and efficiency.
- Advancement of deep learning for fire detection under resource constraints: the lightweight design of AH-YOLO makes it suitable for real-time fire detection in large buildings with limited computational resources.
- Specialized infrared fire dataset for aircraft hangars: the dataset developed for this study aids future advancements in fire detection technology, particularly for aviation safety.
2. Methodology
2.1. YOLOv8
2.2. AH-YOLO: Model Overview
2.2.1. MobileOne
2.2.2. CBAM
2.2.3. Dynamic Head
2.3. Dataset Construction and Experimental Procedure
3. Experiment and Analysis
3.1. Experimental Environment and Hyperparameter Setting
3.2. Evaluation Indicators
3.3. Model Evaluation
3.3.1. Ablation Experiments
3.3.2. Comparative Experiments
- Zhang et al. [40] developed a fire detection model (MobileOne-YOLO) for aircraft cargo compartments by integrating FReLU and EIoU, achieving 0.926 mAP@0.5 and 110 FPS. Though a similar scenario, its accuracy and speed were lower than those of AH-YOLO.
- Huynh et al. [41] fine-tuned YOLOv10 with SE attention and SGD for factory fires, reaching 0.911 mAP but only 78 FPS.
- Wang et al. [42] enhanced YOLOv5s for forest fires using ASPP and CBAM, but reported modest results (0.841 mAP, 70 FPS) under outdoor clutter.
- Liu et al. [43] incorporated CBAM into YOLOv8 for general fire detection, achieving 0.831 mAP with 150 FPS, showing limitations in accuracy despite a decent speed.
- Geng et al. [44] constructed YOLOv9-CBM with SE and BiFPN modules, improving accuracy (0.871 mAP), but at the cost of the real-time performance (35 FPS).
- Zhang et al. [45] introduced GhostNetV2 and SCConv into YOLOv8n for shipboard detection, reaching 0.895 mAP and 190 FPS, but its reliability under complex visibility conditions remains uncertain.
3.3.3. Model Comparison and Visualization
4. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Name | Definition |
---|---|
Processor | Intel(R)Core (TM)i9-13900CPU@3.70GHz |
Running Memory | 128G |
Operating System | Windows |
GPU | NVIDIA GeForce RTX 4090 |
GPU Memory | 128G |
Programming Tool | PyCharm 2023.3.2 |
Programming Language | Python 3.9.19 |
Deep Learning Framework | PyTorch 2.0.0 (CUDA 11.8) |
Name | Definition |
---|---|
epoch | 300 |
batch size | 16 |
optimizer | SGD |
lr0 | 0.01 |
weight_decay | 0.0005 |
momentu | 0.937 |
image size | 640 |
ratio | 0.7 |
Model | Precision | Recall | mAP | Param/106 | GFLOPs | FPS |
---|---|---|---|---|---|---|
YOLOv8n | 0.831 | 0.811 | 0.902 | 3.01 | 8.2 | 142 |
YOLOv8n-MobileOne | 0.823 | 0.832 | 0.911 | 2.83 | 7.8 | 157 |
YOLOv8-MobileOne-CBAM | 0.858 | 0.853 | 0.928 | 2.84 | 7.8 | 144 |
YOLOv8-MobileOne-CBAM-Dyhead | 0.891 | 0.869 | 0.938 | 2.54 | 7.3 | 169 |
Models | Precision | Recall | mAP | Param/106 | GFLOPs | FPS |
---|---|---|---|---|---|---|
YOLOv5n | 0.831 | 0.783 | 0.882 | 2.50 | 7.1 | 158 |
YOLOv7-tiny | 0.852 | 0.837 | 0.901 | 6.01 | 13.0 | 127 |
RepViT | 0.871 | 0.793 | 0.897 | 4.12 | 11.8 | 118 |
ShuffleNetV2 | 0.826 | 0.784 | 0.886 | 2.79 | 7.4 | 155 |
EfficientNetV2 | 0.901 | 0.823 | 0.916 | 21.75 | 55.1 | 101 |
MobileNetV3 | 0.877 | 0.778 | 0.907 | 5.65 | 10.7 | 131 |
YOLO-v8n | 0.832 | 0.812 | 0.904 | 3.01 | 8.2 | 140 |
YOLOv9-tiny | 0.836 | 0.792 | 0.892 | 1.97 | 7.6 | 168 |
YOLOv10n | 0.842 | 0.802 | 0.898 | 2.69 | 8.2 | 176 |
YOLO11n | 0.873 | 0.789 | 0.901 | 2.59 | 6.4 | 170 |
Our | 0.890 | 0.875 | 0.938 | 2.54 | 7.3 | 171 |
Indicators | Our | Distributed Optical Fiber Heat Detectors [46] | Thermal Resistance Sensors [47] | Miscellaneous Heat Detectors [48] |
---|---|---|---|---|
Detection Element | Infrared camera | Two parallel optical fibers | Ammonium polyphosphate and GO | Thermocouple And digital multimeter |
Construction and Working Principle | AH-YOLO Model | By measuring the temperature of hot air flows | Freeze-drying | Operational algorithm |
Response Time | 5.9 ms | 40 s | 2.6 s | 60–120 s |
Detection Area | 0–50 m | Wide ranges | Small | Small |
Features and Advantages | Efficient in low visibility Low-latency response | Simple and efficient | Compressible | Useful where temperature varies |
Model | False Count | Miss Count | Test Sample | False Rate (%) | Miss Rate (%) |
---|---|---|---|---|---|
YOLOv8 | 31 | 6 | 500 | 6.2% | 1.2% |
Ah-YOLO | 14 | 3 | 500 | 2.8% | 0.6% |
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Share and Cite
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
Deng L, Wang Z, Liu Q. AH-YOLO: An Improved YOLOv8-Based Lightweight Model for Fire Detection in Aircraft Hangars. Fire. 2025; 8(5):199. https://doi.org/10.3390/fire8050199
Chicago/Turabian StyleDeng, Li, Zhuoyu Wang, and Quanyi Liu. 2025. "AH-YOLO: An Improved YOLOv8-Based Lightweight Model for Fire Detection in Aircraft Hangars" Fire 8, no. 5: 199. https://doi.org/10.3390/fire8050199
APA StyleDeng, L., Wang, Z., & Liu, Q. (2025). AH-YOLO: An Improved YOLOv8-Based Lightweight Model for Fire Detection in Aircraft Hangars. Fire, 8(5), 199. https://doi.org/10.3390/fire8050199