Realtime fire detection using CNN and search space navigation

Rahmatov, Nematullo; Paul, Anand; Saeed, Faisal; Seo, Hyuncheol

doi:10.1007/s11554-021-01153-4

Cited by 11 publications

(5 citation statements)

References 23 publications

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“…Fast and accurate judgment depends on the accurate identification of risk warning modes for college students' safety prevention, including the construction and integration of functional modules such as data collection systems, information analysis and prediction systems, and decision alarm systems. (6) This paper will mainly focus on two kinds of campus safety precautions, mask detection and boundary detection. Among them, mask detection can effectively help students prevent the epidemic, and boundary detection can prevent students from approaching the dangerous area.…”

Section: Problem Backgroundmentioning

confidence: 99%

High Response Speed and Accuracy Real-Time Mask-Detection System for Chinese Campuses

Zhong,

Mohamad Sharif,

Salam

et al. 2024

Salud, Ciencia y Tecnología - Serie de Conferencias

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Due to the increasing number of students studying in universities globally, the need for effective and timely safety measures has become more critical. This study aims to provide a high tech monitoring system that can help universities realize the security they need. The main functions are mask detection. Among them, mask detection is mainly used to determine if students are wearing the right masks. This paper also carried out algorithm provinciation for two kinds of detection.In the mask detection function, YOLOV4-Tiny model is used, and SPP is added and improved on this basis. And replace the feature enhancement network with the path aggregation network (PAN). After the experiment, the accuracy was improved, Precision (P) and Recall (R) increase by 1.61% and 4.14%.and the response speed of mask detection was improved(The FPS reached 98.67) too. It greatly improves the efficiency of the system and provides security for students.

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Section: Problem Backgroundmentioning

confidence: 99%

High Response Speed and Accuracy Real-Time Mask-Detection System for Chinese Campuses

Zhong,

Mohamad Sharif,

Salam

et al. 2024

Salud, Ciencia y Tecnología - Serie de Conferencias

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“…Rahmatov et al [93] proposed a fire prevention method over large forest areas. At first, a CNN detected the fire and its geographic location.…”

Section: Post-fire Mapping Based Satellite Remote Sensing Imagerymentioning

confidence: 99%

“…Then, an intelligent agent determined the optimal path to reach the forest fire's location at the optimal time. The analysis of the results showed the efficiency of this method to help in controlling disaster situations and reduce the damage caused by the fire [93]. Khennou et al [94] also proposed a forest fire spread prediction model based on U-Net called FU-Netcast.…”

Section: Post-fire Mapping Based Satellite Remote Sensing Imagerymentioning

confidence: 99%

Wildfire Segmentation Using Deep Vision Transformers

Ghali

Akhloufi

Jmal³

et al. 2021

Remote Sensing

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In this paper, we address the problem of forest fires’ early detection and segmentation in order to predict their spread and help with fire fighting. Techniques based on Convolutional Networks are the most used and have proven to be efficient at solving such a problem. However, they remain limited in modeling the long-range relationship between objects in the image, due to the intrinsic locality of convolution operators. In order to overcome this drawback, Transformers, designed for sequence-to-sequence prediction, have emerged as alternative architectures. They have recently been used to determine the global dependencies between input and output sequences using the self-attention mechanism. In this context, we present in this work the very first study, which explores the potential of vision Transformers in the context of forest fire segmentation. Two vision-based Transformers are used, TransUNet and MedT. Thus, we design two frameworks based on the former image Transformers adapted to our complex, non-structured environment, which we evaluate using varying backbones and we optimize for forest fires’ segmentation. Extensive evaluations of both frameworks revealed a performance superior to current methods. The proposed approaches achieved a state-of-the-art performance with an F1-score of 97.7% for TransUNet architecture and 96.0% for MedT architecture. The analysis of the results showed that these models reduce fire pixels mis-classifications thanks to the extraction of both global and local features, which provide finer detection of the fire’s shape.

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“…Intelligent search techniques and an intelligent agent for smart search are useful in many application domains. In the contribution by Seo et al "Realtime fire detection using CNN and search space navigation," the authors develop a state space navigational model for intelligent agents aimed at industrial surveillance from fire hazards [14]. They focus is on fire detection using the convolution neural network then proactively search the area which are more likely to have routes toward the target.…”

Section: Applicationsmentioning

confidence: 99%