Performance Analysis of Support Vector Machine (SVM) on Challenging Datasets for Forest Fire Detection

This article delves into the analysis of performance and utilization of Support Vector Machines (SVMs) for the critical task of forest fire detection using image datasets. With the increasing threat of forest fires to ecosystems and human settlements, the need for rapid and accurate detection systems is of utmost importance. SVMs, renowned for their strong classification capabilities, exhibit proficiency in recognizing patterns associated with fire within images. By training on labeled data, SVMs acquire the ability to identify distinctive attributes associated with fire, such as flames, smoke, or alterations in the visual characteristics of the forest area. The document thoroughly examines the use of SVMs, covering crucial elements like data preprocessing, feature extraction, and model training. It rigorously evaluates parameters such as accuracy, efficiency, and practical applicability. The knowledge gained from this study aids in the development of efficient forest fire detection systems, enabling prompt responses and improving disaster management. Moreover, the correlation between SVM accuracy and the difficulties presented by high-dimensional datasets is carefully investigated, demonstrated through a revealing case study. The relationship between accuracy scores and the different resolutions used for resizing the training datasets has also been discussed in this article. These comprehensive studies result in a definitive overview of the difficulties faced and the potential sectors requiring further improvement and focus.

A New Fire Detection Method Using a Multi-Expert System Based on Color Dispersion, Similarity and Centroid Motion in Indoor Environment

In this paper, a video fire detection method is proposed, which demonstrated good performance in indoor environment. Three main novel ideas have been introduced. Firstly, a flame color model in RGB and HIS color space is used to extract pre-detected regions instead of traditional motion differential method, as it’s more suitable for fire detection in indoor environment. Secondly, according to the flicker characteristic of the flame, similarity and two main values of centroid motion are proposed. At the same time, a simple but effective method for tracking the same regions in consecutive frames is established. Thirdly,a multi-expert system consisting of color component dispersion,similarity and centroid motion is established to identify flames.The proposed method has been tested on a very large dataset of fire videos acquired both in real indoor environment tests and from the Internet. The experimental results show that the proposed approach achieved a balance between the false positive rate and the false negative rate, and demonstrated a better performance in terms of overall accuracy and F standard with respect to other similar fire detection methods in indoor environment.

Sequential Pattern Technology for Visual Fire Detection

Visual fire detection technologies can detect fire and alarm warnings earlier than conventional fire detectors. This study proposes an effective visual fire detection method that combines the statistical fire color model and sequential pattern mining technology to detect fire in an image. Furthermore, the proposed method also supports real-time fire detection by integrating adaptive background subtraction technologies. Experimental results show that the proposed method can effectively detect fire in test images and videos. The detection accuracy of the proposed hybrid method is better than that of Celik＇s method.

Forest Fire Detection Using Artificial Neural Network Algorithm Implemented in Wireless Sensor Networks

A forest fire is a severe threat to forest resources and human life, In this paper, we propose a forest-fire detection system that has an artificial neural network algorithm implemented in a wireless sensor network （WSN）. The proposed detection system mitigates the threat of forest fires by provide accurate fire alarm with low maintenance cost. The accuracy is increased by the novel multi- criteria detection, referred to as an alarm decision depends on multiple attributes of a forest fire. The multi-criteria detection is implemented by the artificial neural network algorithm. Meanwhile, we have developed a prototype of the proposed system consisting of the solar batter module, the fire detection module and the user interface module.

A Review on Mine Fire Disasters and Assessment of Fire Detection Using a Dual-Cab Suppression System

The health and productivity of mining operations are negatively impacted by coal mine fires, making them dangerous. It happened everywhere, in both working and abandoned coal mines. This study seeks to review and provide technical analytics of potential mine fires and fire detection in a Dual-Cab suppression system. Analysis was done on potential mine fires like spontaneous combustion, flammable gas explosions, and cab vehicle fires. Additionally, a review of the NIOSH experiment was conducted to assess the performance of smoke and flame detectors in a dual-cab suppression system. This study guides both open-pit and underground mining operations. Additionally, a few ideas and suggestions are presented to assist with on-the-job safety analysis, ensuing creative alterations, and technology advancement for the mining industry’s overall safety.

Video Based Fire Detection Systems on Forest and Wildland Using Convolutional Neural Network

The devastating effects of wildland fire are an unsolved problem,resulting in human losses and the destruction of natural and economic resources.Convolutional neural network(CNN)is shown to perform very well in the area of object classification.This network has the ability to perform feature extraction and classification within the same architecture.In this paper,we propose a CNN for identifying fire in videos.A deep domain based method for video fire detection is proposed to extract a powerful feature representation of fire.Testing on real video sequences,the proposed approach achieves better classification performance as some of relevant conventional video based fire detection methods and indicates that using CNN to detect fire in videos is efficient.To balance the efficiency and accuracy,the model is fine-tuned considering the nature of the target problem and fire data.Experimental results on benchmark fire datasets reveal the effectiveness of the proposed framework and validate its suitability for fire detection in closed-circuit television surveillance systems compared to state-of-the-art methods.

Automatization of Forest Fire Detection Using Geospatial Technique

Healthy forest is the vital resource to regulate climate at a regional and global level. Forest fire has been regarded as one of the major reasons for the loss of forest and degradation of the environment. Global warming is increasing its intensity at an alarming rate. Real-time fire detection is a necessity to avoid large scale losses. Remote sensing is a quick and cheap technique for detecting and monitoring forest fires on a large scale. Advance Very Radiometer Resolution (AVHRR) has been used already for a long period for fire detection. The use of Moderate Resolution Imaging Radio Spectrometer (MODIS) for fire detection has recently preceded AVHRR and a large number of fire products are being developed. MODIS based forest fire detection and monitoring system can solve the problem of real-time forest fire monitoring. The system facilitates data acquisition, processing, reporting and feedback on the fire location information in an automated manner. It provides location information at 1 × 1 kilometer resolution on the active fires which are present during the satellite overpass twice a day. The users are provided with the information on SMS alert with fire location details, email notification, and online visualization of fire locations on website automatically. The whole processes are automated and provide better accuracy for fire detection.

One Fire Detection Method Using Neural Networks

A neural network fire detection method was developed using detection information for temperature smoke density, and CO concentration to determine the probability of three representative fire conditions. The method overcomes the shortcomings of domestic fire alarm systems using single sensor information. Test results show that the identification error rates for fires, smoldering fires, and no fire are less than 5%, which greatly reduces leak-check rates and false alarms. This neural network fire alarm system can fuse a variety of sensor data and improve the ability of systems to adapt in the environment and accurately predict fires, which has great significance for life and property safety.

基于FireNet的古建筑火灾检测方法研究及改进

针对古建筑火灾检测需要快速、准确及实时的需求,建立了一个专门用于古建筑火灾检测的数据集,用于古建筑火灾检测的深度学习研究。利用CBAM注意力机制模块,结合多尺度特征融合,对FireNet网络进行改进,提出适用于古建筑火灾检测的轻量级FireNet-AMF网络,在FireNet数据集和本文构建的古建筑火灾检测数据集上验证了FireNet-AMF网络的火灾检测能力。与改进前的网络相比,FireNet-AMF网络在FireNet数据集上对火灾识别的准确率达到了95.08%,与原网络相比提高了1.17%,在本文构建的古建筑火灾检测数据集上的准确率达到了95.62%,比原网络提高了1.62%。该网络在保证轻量级的同时也保证了在古建筑火灾检测中较高的检测精度。

PW306C发动机清洗后出现ENG FIRE DETECT FAIL R警告的分析

发动机火警系统属于威胁飞行安全的核心风险之一。本文针对一起PW306C发动机清洗后出现ENG FIRE DETECT FAIL R警告的故障,还原故障现象,结合发动机火警原理图阐述PW306C发动机火警系统工作原理,为故障的深入分析提供理论基础;在故障分析环节,排除法锁定故障部件,通过实验还原证实了故障原因,确保飞机放行不留任何隐患。

Forest Fire Smoke Detection Method Based on MoAm-YOLOv4 Algorithm

To improve the performance of the forest fire smoke detection model and achieve a better balance between detection accuracy and speed, an improved YOLOv4 detection model (MoAm-YOLOv4) that combines a lightweight network and attention mechanism was proposed. Based on the YOLOv4 algorithm, the backbone network CSPDarknet53 was replaced with a lightweight network MobilenetV1 to reduce the model’s size. An attention mechanism was added to the three channels before the output to increase its ability to extract forest fire smoke effectively. The algorithm used the K-means clustering algorithm to cluster the smoke dataset, and obtained candidate frames that were close to the smoke images;the dataset was expanded to 2000 images by the random flip expansion method to avoid overfitting in training. The experimental results show that the improved YOLOv4 algorithm has excellent detection effect. Its mAP can reach 93.45%, precision can get 93.28%, and the model size is only 45.58 MB. Compared with YOLOv4 algorithm, MoAm-YOLOv4 improves the accuracy by 1.3% and reduces the model size by 80% while sacrificing only 0.27% mAP, showing reasonable practicability.

A Neural Based Experimental Fire-Outbreak Detection System for Urban Centres

Incessant fire-outbreak in urban settlements has remained intractable especially in developing countries like Nigeria. This is often characterized by grave socio-economic aftermath effects. Urban fire outbreak in Nigerian cities has been on increase in recent times. The major problem faced by fire fighters in Nigerian urban centres is that there are no mechanisms to detect fire outbreaks early enough to save lives and properties. They often rely on calls made by neighbours or occupants when an outbreak occurs and this accounts for the delay in fighting fire outbreaks. This work uses Artificial Neural Networks (ANN) with backpropagation method to detect the occurrence of urban fires. The method uses smoke density, room temperature and cooking gas concentration as inputs. The work was implemented using Java programming language and results showed that it detected the occurrence of urban fires with reasonable accuracy. The work is recommended for use to minimize the effect of urban fire outbreak.

基于改进FireNet的轻型火灾实时检测方法

针对火灾检测精确不高,时间长等问题。设计了基于改进FireNet的轻型火灾实时检测方法,通过获取视频图像数据,网络模型进行火灾分析和识别;首先,在FireNet特征提取阶段使用多尺度卷积网络并引入通道注意力机制,以提高回归精度。其次,对全连接层的神经元个数进行压缩优化,减少计算耗时。实验表明,改进的FireNet算法模型检测精度达到96.43%,模型存储空间0.96MB,检测帧率37。相比标准算法精度提高2.5%,存储空间压缩85%,检测帧率提升35%。

Rapid Detection of Accelerants in Fire Debris Using a Field Portable Mid-Infrared Quantum Cascade Laser Based Analyzer

Arson presents a challenging crime scene for fire investigators worldwide. Key to the investigation of suspected arson cases is the analysis of fire debris for the presence of accelerants or ignitable liquids. This study has investigated the application and method development of vapor phase mid-Infrared (mid-IR) spectroscopy using a field portable quantum cascade laser (QCL) based system for the detection and identification of accelerant residues such as gasoline, diesel, and ethanol in fire debris. A searchable spectral library of various ignitable fluids and fuel components measured in the vapor phase was constructed that allowed for real-time identification of accelerants present in samples using software developed in-house. Measurement of vapors collected from paper material that had been doused with an accelerant followed by controlled burning and then extinguished with water showed that positive identification could be achieved for gasoline, diesel, and ethanol. This vapor phase mid-IR QCL method is rapid, easy to use, and has the sensitivity and discrimination capability that make it well suited for non-destructive crime scene sample analysis. Sampling and measurement can be performed in minutes with this 7.5 kg instrument. This vibrational spectroscopic method required no time-consuming sample pretreatment or complicated solvent extraction procedure. The results of this initial feasibility study demonstrate that this portable fire debris analyzer would greatly benefit arson investigators performing analysis on-site.

Danger Detection during Fight against Compartment-Fire Using Moving Averages in Temperature Recordings

In compartment fires (houses, buildings, underground, warehouse, etc.), smokes are a major dan- ger during firemen intervention. Most of the time, they are at high temperature (>800?C) and they flow everywhere through many kinds of ducts, which leads to the propagation of the combustion by the creation other fires in places which may be far away from the initial fire. In this paper, we present a new approach of the problem, which allows to better follow the fire behavior and especially to detect the dangers that may appear and endanger firefighters. This approach consists in a mathematical analysis based on the comparison of moving averages centered in the past, calculated on the temperature recordings of the smokes. As a consequence, this method may allow to improve decision support in real time and therefore to improve the security and the efficiency of firefighters in their operations against that kind of fires.

Fire&Gas监测系统设计及应用

通过国外某项目的应用案例,介绍了Fire&Gas监测系统的基本组成和主要功能,提出了对Fire&Gas监测系统的基本要求。其目的是为设计人员提供Fire&Gas监测系统的设计方法和其他需要关注的问题。

扩展Fire单个单向突发错误纠正／全部单向错误检测码的构造

给出了构造１－ｌＵＢＥＣ／ＡＵＥＤ（单个单向突发错误纠正／全部单向错误检测）码的充分必要条件和建议的１－ｌＵＢＥＣ／ＡＵＥＤ码校验位的下限；将纠单个突发错误Ｆｉｒｅ码进行扩展，得到了扩展Ｆｉｒｅ１－ｌＵＢＥＣ／ＡＵＥＤ码；证明了扩展Ｆｉｒｅ码为无序码和１－ｌＵＢＥＣ／ＡＵＥＤ码；最后给出了基于移位寄存器实现扩展Ｆｉｒｅ码的编－译码框图。在纠单向突发错误和检单向错误时，扩展Ｆｉｒｅ码具有更高的译码效率和信息率。

基于多尺度特征融合的轻量级火灾检测算法

针对传统火灾检测算法存在的检测精度不足及速度瓶颈,特别是对于小规模初发火情与大规模迅速蔓延火灾的识别难题,研究提出一种基于多尺度特征融合的轻量级火灾检测算法,设计了EDBAN模块以替代YOLOv8中的C2f模块,提升模型的泛化能力和适应性,尤其是在处理多尺度火灾场景时的精准度。改进原有的BiFPN结构适配YOLOv8模型结构,并设计Weighted Blend模块对各层特征进行加权融合,增强特征的表征能力,降低漏检风险。进一步提出LOTT检测模块,以替代传统的YOLOv8检测,通过一系列组卷积和尺度调整操作,实现了在轻量化的同时保持了检测性能的准确性和稳定性。通过在场景丰富的火灾数据集上进行实验,结果表明,改进的YOLOv8算法在基准模型的基础上参数量减少了58.3%、计算量减少了34.5%,同时mAP提升了2.6个百分点,基本满足火灾实时检测的需求。

基于YOLOv7的红外阴燃火探测算法改进研究

目前,基于机器视觉的火灾检测算法中数据集类型不充分、数据集在时间维度覆盖不全面,致使此类算法难以实现火灾的早期预警,文中提出了基于改进YOLOv7的红外阴燃探测方法.该算法利用EfficientFormerV2模型替换原模型的骨干网络CSPDarknet53,从而增强了模型低延迟、低参数量、易部署的能力;同时,在预测网络中,采用CARAFE轻量化上采样模块代替原模型中的上采样模块,扩大了模型对特征的感受野,改善了阴燃特征的表示能力;此外,还引入了新的NWD度量来提升模型边界框预测能力.结果表明,在自建阴燃数据集上,该算法的平均精度达到92.9%,对阴燃检测的平均精度达到99.6%,比YOLOv7的精度提升了14.4%,较基于手工提取特征的卷积神经网络算法提升了4.6%.研究成果将为阴燃火早期预警提供新思路.

基于无人机多光谱遥感的林火监测模型

【目的】森林火灾监测多采用卫星和低空热红外遥感对林火进行识别,准确率高,但受限于硬件性能和成本,对基于无人机的多光谱遥感及不同图像传感器比较的林火监测研究较少。【方法】选定山地树林作为试验对象,根据起火点的明火和阴燃两种状态,结合树冠状态,分为明火有遮挡、明火无遮挡、阴燃有遮挡、阴燃无遮挡等4种林火状态,以无火场景作为对照,开展森林火灾监测试验,利用无人机分别搭载热红外、多光谱、可见光等图像传感器采集林火图像,分别基于随机森林(RF)、支持向量机(SVM)、反向传播神经网络(BP)3种机器学习算法建立林火监测模型,通过准确率(Accuracy)、精度(Precision)、召回率(Recall)和F1-score进行监测模型性能评估。【结果】综合分析,热红外相机和可见光相机基于支持向量机(SVM)的监测模型准确率最高,多光谱相机基于随机森林(RF)的监测模型准确率最高。热红外相机监测准确率高达100%,多光谱相机接近100%,可见光相机达到85%。综合分析,热红外相机监测准确率最高,多光谱相机次之,可见光相机监测性能最差。多光谱相机可在不同林火状态下较好地替代热红外相机进行监测,可见光相机在不同林火状态下均表现出较差的监测效果。【结论】通过使用机器学习算法进行优化,多光谱相机可在林火监测中有效替代热红外相机,可以显著降低监测成本和丰富林火监测技术手段。