A Wavelet-Based Real-Time Fire Detection with Multi-Modeling Framework

Baek, Jae-Seung; Alhindi, Taha J.; Jeong, Young-Seon; Jeong, Myong K.; Seo, Seongho; Kang, Jahyo; Shim, We; Heo, Yoseob

doi:10.2139/ssrn.4333778

Cited by 1 publication

(2 citation statements)

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“…However, these algorithms have many deficiencies in detection accuracy, real-time and cost, which makes the research on forest fire detection increasingly prominent. 4 With the rapid development of the Internet of Things and computer vision technology, 5 forest fire detection based on video surveillance has achieved better performance than traditional forest fire detection. Video monitoring makes full use of data collected by cameras, combined with video analysis methods, to effectively detect pixel changes in video scenes, thereby achieving the positioning and recognition of forest fire objects.…”

Section: Introductionmentioning

confidence: 99%

“…To realize the efficient and rapid detection of forest fires, the early methods can be divided into the following categories: satellite remote sensing detection, aerial patrol, human patrol and near‐ground monitoring (watch tower timing observation). However, these algorithms have many deficiencies in detection accuracy, real‐time and cost, which makes the research on forest fire detection increasingly prominent 4 …”

Section: Introductionmentioning

confidence: 99%

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An effective graph embedded YOLOv5 model for forest fire detection

Yuan,

Lu,

Zhang

et al. 2024

Computational Intelligence

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%