LSKA-YOLOv8n-WIoU: An Enhanced YOLOv8n Method for Early Fire Detection in Airplane Hangars

An aircraft hangar is a special large-space environment containing a lot of combustible materials and high-value equipment. It is essential to quickly and accurately detect early-stage fires when they occur. In this study, experiments were conducted in a real aircraft hangar to simulate the occurrence of early-stage fires, and the collected images were classified, labeled, and organized to form the dataset used in this paper. The fire data in the dataset were categorized into two target classes: fire and smoke. This study proposes an aircraft hangar fire detection method that integrates an attention mechanism, which was based on the You Only Look Once Version 8 Nano (YOLOv8n) framework and further improved. Technically, the optimization of YOLOv8n was mainly carried out in two stages: Firstly, at the network structure level, the neck network of YOLOv8n was reconstructed using a large separable kernel attention (LSKA) module; secondly, in terms of loss function design, the original CIoU loss function was replaced with a dynamic focus-based Wise-IoU to enhance the detection performance of the model. This new algorithm is named LSKA-YOLOv8n+WIoU. Experimental results show that the LSKA-YOLOv8n+WIoU algorithm has superior fire detection performance compared to related state-of-the-art algorithms. Compared to the YOLOv8n model, the precision increased by 10% to 86.7%, the recall increased by 8.8% to 67.2%, and the mean average precision (mAP) increased by 5.9% to 69.5%. The parameter size was reduced by 0.5MB to 5.7MB. Through these improvements, the accuracy of flame and smoke detection was enhanced while reducing computational complexity, increasing computational efficiency, and effectively mitigating the phenomena of missed and false detections. This study contributes to enhancing the accuracy and speed of fire detection systems used in aircraft hangar environments, providing reliable support for early-stage aircraft hangar fire alarm work.

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Publication Details

Journal
Fire
Published
2025-02-07
DOI
https://doi.org/10.3390/fire8020067
Citations
4
Primary Topic
Fire Detection and Safety Systems
Type
article
Field-Weighted Citation Impact
5.21

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article

LSKA-YOLOv8n-WIoU: An Enhanced YOLOv8n Method for Early Fire Detection in Airplane Hangars

Quanyi Liu, Li Deng, Wu Siqi, Jin Zhou et al.
4 citations
Fire
Fire Detection and Safety Systems
5.21
article

LSKA-YOLOv8n-WIoU: An Enhanced YOLOv8n Method for Early Fire Detection in Airplane Hangars

Quanyi Liu, Li Deng, Wu Siqi, Jin Zhou, Shuang Zou
article en
4 citations

Abstract

An aircraft hangar is a special large-space environment containing a lot of combustible materials and high-value equipment. It is essential to quickly and accurately detect early-stage fires when they occur. In this study, experiments were conducted in a real aircraft hangar to simulate the occurrence of early-stage fires, and the collected images were classified, labeled, and organized to form the dataset used in this paper. The fire data in the dataset were categorized into two target classes: fire and smoke. This study proposes an aircraft hangar fire detection method that integrates an attention mechanism, which was based on the You Only Look Once Version 8 Nano (YOLOv8n) framework and further improved. Technically, the optimization of YOLOv8n was mainly carried out in two stages: Firstly, at the network structure level, the neck network of YOLOv8n was reconstructed using a large separable kernel attention (LSKA) module; secondly, in terms of loss function design, the original CIoU loss function was replaced with a dynamic focus-based Wise-IoU to enhance the detection performance of the model. This new algorithm is named LSKA-YOLOv8n+WIoU. Experimental results show that the LSKA-YOLOv8n+WIoU algorithm has superior fire detection performance compared to related state-of-the-art algorithms. Compared to the YOLOv8n model, the precision increased by 10% to 86.7%, the recall increased by 8.8% to 67.2%, and the mean average precision (mAP) increased by 5.9% to 69.5%. The parameter size was reduced by 0.5MB to 5.7MB. Through these improvements, the accuracy of flame and smoke detection was enhanced while reducing computational complexity, increasing computational efficiency, and effectively mitigating the phenomena of missed and false detections. This study contributes to enhancing the accuracy and speed of fire detection systems used in aircraft hangar environments, providing reliable support for early-stage aircraft hangar fire alarm work.

FireVol. 8(2)
Civil Aviation Flight University of China (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 6%
Fire Detection and Safety Systems
5.21
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