航空科学技术2024，Vol.35Issue(11)：112-118,7.DOI:10.19452/j.issn1007-5453.2024.11.014
基于改进YOLO模型的飞机货舱红外图像火灾检测
Aircraft Cargo Compartment Fire Detection with Infrared Images Based on Improved YOLO Model
摘要
Abstract
Owing to the swift evolution of information technology,infrared detection techniques and video surveillance systems have witnessed extensive utilization,wherein image-based fire detectors are increasingly proving their merits in fire detection.In the realm of aircraft cargo compartment fire detection,despite the demonstrated potential of image-based fire detection technology,the equilibrium between precision and responsiveness necessitates further refinement.To bolster the capacity to identify and evaluate incipient fires within aircraft cargo compartments and augment the precision of infrared flame image target detection,this study introduces a refined YOLO(You Only Look Once)target detection algorithm integrated with an enhanced loss function.Initially,a comparison was made on the performance of multiple typical target detection algorithms in infrared flame image detection tasks,leading to the selection of a suitable algorithmic framework for the improvement of the loss function.Initially,a comparison was made on the performance of multiple typical target detection algorithms in infrared flame image detection tasks,leading to the selection of a suitable algorithmic framework for the improvement of the loss function.By meticulously accounting for variables like the distance between target centers,overlap area,and aspect ratio during loss computation,we crafted an enhanced loss function and effectively incorporated the weighted intersection over union(WIoU)loss function based on dynamic nonmonotonic focusing mechanism into the YOLO target detection network thereby bolstering detection accuracy.Experimental evaluations on infrared flame image datasets indicate that the enhanced YOLOv5 algorithm did not yield substantial gains in performance,whereas the YOLOv7 algorithm,after the introduction of the enhanced loss function,exhibited a 2.1%surge in detection accuracy,a 6.5%enhancement in mean average precision(mAP),and a 2.68-frame boost in frames per second(FPS).With regard to crucial performance metrics like box loss,objectness loss,and total loss,the YOLOv7 model utilizing the WIoU loss function excelled over other models,attaining the minimum loss value.In summary,the YOLOv7 algorithm with an enhanced loss function presented in this research demonstrates superior accuracy and responsiveness,offering a potent technical approach for aircraft cargo compartment fire detection.关键词
YOLO/飞机货舱/目标检测/WIoU/火焰红外图像Key words
YOLO/aircraft cargo compartment/target detection/WIoU/flame infrared image分类
资源环境引用本文复制引用
邓力,谢爽爽,刘全义,谭阳..基于改进YOLO模型的飞机货舱红外图像火灾检测[J].航空科学技术,2024,35(11):112-118,7.基金项目
国家自然科学基金(U2033206) （U2033206）
航空科学基金(20200046117001) （20200046117001）
四川省重点实验室项目(MZ2022JB01) National Natural Science Foundation of China(U2033206) （MZ2022JB01）
Aeronautical Science Foundation of China(20200046117001) （20200046117001）
Sichuan Provincial Key Laboratory Project(MZ2022JB01) （MZ2022JB01）
