Research on lightweight forest fire detection algorithm based on YOLOv5s

doi:10.11996/JG.j.2095-302X.2023010026

Abstract

Abstract:

A new algorithm for light-weight forest fire object detection was proposed based on YOLOv5s to address the low accuracy, poor flexibility, and high software and hardware limitations of the previous UAV-embedded equipment for forest fire inspection. The proposed algorithm replaced the backbone of YOLOv5s with the light-weight network Shufflenetv2, employed the idea of channel recombination to improve the speed of the backbone network in picture information extraction, and maintained both high accuracy and fast detection speed. Then, a coordinate attention (CA) positional attention module specially designed for light-weight network was added to the connection between Backbone and Neck, which could aggregate different position information of pictures into the channel, thus improving the attention of the detected object. Finally, the CIOU loss function was utilized in the prediction part to better optimize the ratio of length to width of the rectangular frame and accelerate the model convergence. The results of the algorithm deployed on Jetson Xavier NX show that compared with the Faster-RCNN, SSD, YOLOv4, and YOLOv5s experimental methods, the improved network model size was reduced by up to 98%, increasing the precision to 92.6%, accuracy rate to 95.3%, and FPS to 132 frames/s. It can effectively achieve the real-time prevention and detection of forest fire in daylight, darkness, or good visibility, exhibiting good accuracy and robustness.

Key words: object detection, YOLOv5s, light-weight, positional attention module, forest fire detection

CLC Number:

TP391

PI Jun, LIU Yu-heng, LI Jiu-hao. Research on lightweight forest fire detection algorithm based on YOLOv5s[J]. Journal of Graphics, 2023, 44(1): 26-32.

Figures/Tables 11

References 20

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Model summary	Layers	Parameters	Gradients	GFLOPS
CSPDarknet	272	7 233 211	7 233 211	16.4
Shuffnetv2	224	3 608 302	3 608 302	8.1

Model summary	Layers	Parameters	Gradients	GFLOPS
CSPDarknet	272	7 233 211	7 233 211	16.4
Shuffnetv2	224	3 608 302	3 608 302	8.1

Number	Shuffle (Backbone)	CIOU_Loss	SPPF	CA	Precison	Recall	AP
1	√	-	-	-	0.890	0.924	0.935
2	√	√	-	-	0.908	0.889	0.936
3	√	-	√	-	0.907	0.880	0.929
4	√	-	-	√	0.920	0.911	0.931
5	√	√	√	-	0.927	0.912	0.945
6	-	√	√	-	0.912	0.921	0.911
7	-	√	-	√	0.932	0.919	0.942
8	-	√	√	√	0.936	0.932	0.950
9	-	-	√	√	0.922	0.928	0.949
Ours	√	√	√	√	0.941	0.929	0.960

Number	Shuffle (Backbone)	CIOU_Loss	SPPF	CA	Precison	Recall	AP
1	√	-	-	-	0.890	0.924	0.935
2	√	√	-	-	0.908	0.889	0.936
3	√	-	√	-	0.907	0.880	0.929
4	√	-	-	√	0.920	0.911	0.931
5	√	√	√	-	0.927	0.912	0.945
6	-	√	√	-	0.912	0.921	0.911
7	-	√	-	√	0.932	0.919	0.942
8	-	√	√	√	0.936	0.932	0.950
9	-	-	√	√	0.922	0.928	0.949
Ours	√	√	√	√	0.941	0.929	0.960

算法	Backbone	模型大小(MB)	Precision	Recall	AP	FPS	检测用时(ms)
YOLOv4	CSPDarknet53	241.2	0.859	0.758	0.852	35	11.2
YOLOv4-tiny	CSPDarknet53-Tiny	25.3	0.869	0.792	0.843	42	4.5
YOLOv5s	CSPDarknet	11.5	0.923	0.909	0.944	79	9.8
YOLOv5s-P6	CSPDarknet	15.4	0.891	0.881	0.932	62	10.1
YOLOv5s-P7	CSPDarknet	15.5	0.906	0.892	0.939	65	11.3
YOLOv5s-Transformer	Transformer	20.1	0.915	0.911	0.950	70	9.2
SSD	VGGnet	180.3	0.613	0.521	0.631	22	60.7
Faster-RCNN	Resnet	212.3	0.732	0.612	0.756	28	53.1
Ours	Shufflenetv2	5.1	0.926	0.920	0.953	132	3.6