一种无锚框结构的多尺度火灾检测算法

doi:10.19665/j.issn1001-2400.2022.06.014

Abstract

Abstract:

In view of the low detection accuracy of multi-scale flames and false alarms in complex backgrounds,a new fire detection algorithm with an Anchor Free structure is proposed.The algorithm cancels the anchor and adopts a point-by-point prediction method to reduce the hyperparameters of the network,thus effectively reducing the influence of artificial prior knowledge.The BFP module is introduced to optimize feature fusion,and the integration of inter-layer information effectively utilizes the global information on features and enhances the expression ability of multi-scale features.The fusion factor is set to control the information transfer between layers,which ensures the fusion of feature information while reducing the influence of high-level features,and improves the learning ability of shallow features for small targets.A dynamic sampling method is designed to adjust the training process and strengthen the network’s learning effect on flame characteristics by adopting the principle of central sampling and confidence to improve the quality of the sample points.The algorithm not only achieves 96.9% accuracy on the self-built dataset,but also has a good performance on the public fire dataset.Experimental results show that the proposed algorithm has a high detection accuracy and a strong anti-interference ability.The algorithm has a good detection effect for multi-scale flames in complex backgrounds,can better suppress the occurrence of false alarms,and meets the needs of actual fire detection tasks.

Key words: fire detection, anchor free network, multi-scale feature fusion, dynamic sampling

CLC Number:

TP391.41

QIN Rui,ZHANG Wei. Multi-scale fire detection algorithm with an anchor free structure[J].Journal of Xidian University, 2022, 49(6): 111-119.

Figures/Tables 15

References 20

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实验环境	环境条件
CPU	Intel Xeon W-2102
GPU	NVIDIA GeForce RTX 2080 Ti
操作系统	Ubuntu 16.0.7
编程语言	Python 3.7
深度学习框架	Pytorch 1.4.0

采样方式	AP	AP₅₀
标准算法	0.542	0.942
中心采样	0.557	0.945
中心分支^[8]	0.557	0.955
文中算法	0.563	0.957

算法	BFP	融合因子	动态采样	AP	AP₅₀	AP_s	AP_m	AP_l
标准算法				0.542	0.942	0.515	0.563	0.561
	√			0.560	0.953	0.521	0.594	0.595
文中算法	√	√		0.569	0.956	0.542	0.596	0.595
	√	√	√	0.583	0.969	0.554	0.611	0.616

算法类型	算法名称	AP	AP₅₀	参数量	速度/(帧每秒)
	Faster RCNN	0.568	0.955	41.12×10⁶	13.7
Anchor Based	YOlOv3	0.525	0.911	61.53×10⁶	34.9
	RetinaNet	0.545	0.956	31.93×10⁶	15.2
	FSAF	0.541	0.954	31.88×10⁶	15.8
Anchor Free	FoveaBox	0.551	0.948	31.88×10⁶	15.9
	FCOS	0.557	0.955	31.84×10⁶	15.7
	文中算法	0.583	0.969	32.05×10⁶	16.2

算法	误报率	漏报率	准确率
文献[20]	13.33	0.00	92.86
文献[1]	11.67	0.00	93.55
文献[2]	4.83	4.53	95.32
文中算法	4.63	0.00	95.86

Multi-scale fire detection algorithm with an anchor free structure

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References 20

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