A multi-scene fire sign detection algorithm based on EE-YOLOv8s

doi:10.11996/JG.j.2095-302X.2025010013

Abstract

Abstract:

To mitigate the current issues of spurious and missed detections of fire signs in smoke and fire scene detection, caused by interfering factors such as illumination variations, fire dynamics, complex backgrounds, and excessively small targets, an improved YOLOv8s model named EE-YOLOv8s was proposed. The EE-YOLOv8s model integrated the MBConv-Block convolution module into the YOLOv8 Backbone and employed the EfficientNetEasy feature extraction network to refine image feature extraction while preserving a lightweight design. Additionally, the SPPELAN module was upgraded to SPP_LSKA_ELAN by incorporating the large separable kernel attention mechanism (LSKA), which captured spatial detail information in intricate and dynamic fire scenes, thereby distinguishing target objects from convoluted backgrounds. The Neck section introduced deformable convolution (DCN) and cross-space efficient multi-scale attention (EMA), implementing the C2f_DCN_EMA deformable convolution calibration module to enhance the adaptation to edge contour changes of fire and smoke targets, facilitating feature fusion and calibration, and emphasizing key target features. A small target detection head, equipped with the lightweight, parameter-free attention mechanism SimAM, was integrated into the Head section, and the channel configuration was refined to strengthen multi-size target characterization while minimizing redundancy and maximizing parameter utilization efficiency. Experimental results demonstrated that EE-YOLOv8s reduced the parameter count by 13.6%, while improving accuracy by 6.8%, recall by 7.3%, and mAP by 5.4% compared to the original model, ensuring rapid detection speed and superior detection performance for fire targets.

Key words: smoke and fire target detection, EfficientNetEasy backbone network, large separable kernel attention mechanism, deformable convolutional calibration module, small target detection

CLC Number:

TP391

CUI Kebin, GENG Jiachang. A multi-scene fire sign detection algorithm based on EE-YOLOv8s[J]. Journal of Graphics, 2025, 46(1): 13-27.

Figures/Tables 20

References 32

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名称	版本号
Operating System	Windows 11系统
CPU	12th Gen Intel(R) Core(TM) i5-12400F @2.50 GHz
显卡(GPU)	NVIDIA GeForce RTX 4060 Ti (16380 MiB)
Pytorch	torch - 1.13.1 + cu117
GPU accleration CUDA	CUDA 11.7 + cuDNN v8.9.6
Programming Language	Python - 3.9.19

名称	版本号
Operating System	Windows 11系统
CPU	12th Gen Intel(R) Core(TM) i5-12400F @2.50 GHz
显卡(GPU)	NVIDIA GeForce RTX 4060 Ti (16380 MiB)
Pytorch	torch - 1.13.1 + cu117
GPU accleration CUDA	CUDA 11.7 + cuDNN v8.9.6
Programming Language	Python - 3.9.19

Group	主干网络	P/%	R/%	mAP0.50/%	mAP0.75/%	mAP/%	Param/10⁶	GFLOPs	FPS
①	CSPDarkNets	83.6	80.2	89.5	71.8	62.9	11.14	28.6	120.78
②	EfficientNetv2s	85.7	84.0	88.4	72.4	66.3	18.03	40.7	44.73
③	EfficientViT M4	83.1	75.5	85.7	66.6	58.3	10.45	26.4	83.68
④	RepViT M 1.1	85.6	84.3	90.9	62.3	58.9	9.93	28.1	74.37
⑤	FasterNet T1	82.3	76.0	86.1	67.7	59.3	9.40	25.5	106.63
⑥	EfficientNetEasy	85.2	87.8	89.6	67.3	60.0	11.07	27.9	114.96

Group	主干网络	P/%	R/%	mAP0.50/%	mAP0.75/%	mAP/%	Param/10⁶	GFLOPs	FPS
①	CSPDarkNets	83.6	80.2	89.5	71.8	62.9	11.14	28.6	120.78
②	EfficientNetv2s	85.7	84.0	88.4	72.4	66.3	18.03	40.7	44.73
③	EfficientViT M4	83.1	75.5	85.7	66.6	58.3	10.45	26.4	83.68
④	RepViT M 1.1	85.6	84.3	90.9	62.3	58.9	9.93	28.1	74.37
⑤	FasterNet T1	82.3	76.0	86.1	67.7	59.3	9.40	25.5	106.63
⑥	EfficientNetEasy	85.2	87.8	89.6	67.3	60.0	11.07	27.9	114.96

Group	空间金字塔池化模块	P%	R%	mAP/%	Param/10⁶	GFLOPs	FPS
①	SPPF	83.6	80.2	62.9	11.14	28.6	120.78
②	SPPCSPC^[29]	83.6	78.9	61.7	17.55	33.6	124.97
③	SPPFCSPC	83.3	80.5	63.6	17.55	33.6	116.72
④	SPPF_LSKA	88.5	80.4	64.6	12.20	29.3	122.80
⑤	SPPELAN	83.1	84.6	64.4	10.67	27.9	117.12
⑥	SPP_LSKA_ELAN	88.3	85.1	66.4	12.45	29.3	115.46