YOLOv5上融合多特征的实时火焰检测方法

doi:10.16451/j.cnki.issn1003-6059.202206007

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Abstract In natural scenes, the accuracy of fire detection is affected by weather conditions, light intensity and background interference. To achieve real-time accurate fire detection in complex scenarios, a real-time efficient fire detection method based on improved YOLOv5 is proposed. The proposed method is combined with Focal Loss, complete intersection over union loss function and multi-feature fusion to detect fires in real time. The focal loss function is introduced to alleviate the imbalance between positive and negative samples and make full use of the information of difficult samples. Meanwhile, combining the static and dynamic features of fires, a multi-feature fusion method is designed to eliminate false alarm fires. Aiming at the lack of fire datasets at home and abroad, a large-scale and high-quality fire dataset of 100 000 magnitude is constructed(http://www.yongxu.org/databases.html). Experiments show that the accuracy, speed, precision and generalization ability of the proposed method are significantly improved.

Key words： You Only Look Once Version5(YOLOv5) Fire Detection Focal Loss Function Complete Intersection over Union(CIoU) Loss Function Multi-feature Fusion

Received: 16 January 2022

ZTFLH:

TP 389.1

Fund:National Natural Science Foundation of China(No.61971078), Postdoctoral Innovative Talents Support Program of China(No.BX20190100)

Corresponding Authors: XIAO Hanguang, Ph.D., professor. His research interests include image processing, machine learning, multi-mode perception and intelligent information processing.

About author:: ZHANG Dasheng, master student. His research interests include computer vision, image processing and object detection.
WEN Jie, Ph.D., assistant professor. His research interests include machine learning and pattern recognition.
XU Yong, Ph.D., professor. His research interests include pattern recognition, image processing, deep learning, biometric recognition and bio-informatics.

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Cite this article:

ZHANG Dasheng,XIAO Hanguang,WEN Jie等. Real-Time Fire Detection Method with Multi-feature Fusion on YOLOv5[J]. Pattern Recognition and Artificial Intelligence, 2022, 35(6): 548-561.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202206007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2022/V35/I6/548

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