Fire detection methods based on an optimized YOLOv5 algorithm

Shao, Zhenlu; Lu, Siyu; Shi, Xunxian; Yang, Dezhi; Wang, Zhaolong

doi:10.48130/emst-2023-0011

2023

DOI: 10.48130/emst-2023-0011

|View full text |Cite

Fire detection methods based on an optimized YOLOv5 algorithm

Zhenlu Shao,

Siyu Lu,

Xunxian Shi

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

An effective graph embedded YOLOv5 model for forest fire detection

Yuan,

Lu,

Zhang

et al. 2024

Computational Intelligence

View full text Add to dashboard Cite

The existing YOLOv5‐based framework has achieved great success in the field of target detection. However, in forest fire detection tasks, there are few high‐quality forest fire images available, and the performance of the YOLO model has suffered a serious decline in detecting small‐scale forest fires. Making full use of context information can effectively improve the performance of small target detection. To this end, this paper proposes a new graph‐embedded YOLOv5 forest fire detection framework, which can improve the performance of small‐scale forest fire detection using different scales of context information. To mine local context information, we design a spatial graph convolution operation based on the message passing neural network (MPNN) mechanism. To utilize global context information, we introduce a multi‐head self‐attention (MSA) module before each YOLO head. The experimental results on FLAME and our self‐built fire dataset show that our proposed model improves the accuracy of small‐scale forest fire detection. The proposed model achieves high performance in real‐time performance by fully utilizing the advantages of the YOLOv5 framework.

show abstract

An effective graph embedded YOLOv5 model for forest fire detection

Yuan,

Lu,

Zhang

et al. 2024

Computational Intelligence

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Fire detection methods based on an optimized YOLOv5 algorithm

Cited by 1 publication

References 23 publications

An effective graph embedded YOLOv5 model for forest fire detection

An effective graph embedded YOLOv5 model for forest fire detection

Contact Info

Product

Resources

About