Forest Fire Detection Using a Rule-Based Image Processing Algorithm and Temporal Variation

Mahmoud, Mubarak Adam Ishag; Ren, Hongxiang

doi:10.1155/2018/7612487

Cited by 56 publications

(32 citation statements)

References 8 publications

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“…6) of Y, Cb and Cr of a fire region, respectively. [11] observed this characteristic and mentioned that it was verified over countless experiments with images containing fire regions and formulated as the following rule:…”

Section: Rule 1: Ycbcr Intensity Valuesmentioning

confidence: 91%

“…This further alleviates the harmful effects of changing illumination and also detects fire more efficiently; moreover, higher accuracy rates were achieved by utilizing the YCbCr color system over the RGB color system. [11] also performed a research based on the color model approach, to classify a pixel to be fire the model identifies seven rules. If a pixel satisfies those seven rules, then it is classified as fire.…”

Section: Introductionmentioning

confidence: 99%

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Fire Detection Methods Based on Various Color Spaces and Gaussian Mixture Models

Munshi¹

2021

Adv. Sci. Technol. Res. J.

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Fire disasters are very serious problems that may cause damages to ecological systems, infrastructure, properties, and even a threat to human lives; therefore, detecting fi res at their earliest stage is of importance. Inspired by the technological advancements in artifi cial intelligence and image processing in solving problems in diff erent applications, this encourages adopting those technologies in reducing the damage and harm caused by fi re. This study attempts to propose an intelligent fi re detection method by investigating three approaches to detect fi re based on three diff erent color models: RGB, YCbCr, and HSV were presented. The RGB method is applied based on the relationship among the red, green and blue values of pixels in images. In the YCbCr color model, image processing and machine learning techniques are used for morphological processing and automatic recognition of fi re images. In turn, for HSV, supervised machine learning techniques are adopted, namely decision rule and Gaussian mixture model (GMM). Further, the expectation maximization (EM) algorithm was deployed for the GMM parameters estimation. The three proposed models were tested on two data sets, one of which contains fi re images, the other consists of non-fi re images with some having fi re-like colors to test the effi ciency of the proposed methods. The experimental results showed that the overall accuracies on two data sets for the RGB, YCbCr, and HSV methods were satisfactory and were effi cient in detecting the outdoor and indoor fi res.

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Section: Rule 1: Ycbcr Intensity Valuesmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Fire Detection Methods Based on Various Color Spaces and Gaussian Mixture Models

Munshi¹

2021

Adv. Sci. Technol. Res. J.

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“…MUSLU, G. et al introduced an algorithm for nighttime vehicle taillight detection in [28]. This approach is a fusion of the Haar Cascade Classifier [29] [30] and the processing of images based on rules [31]. Haar [32], which is used for vehicle identification and vehicle taillight or vehicle rear vision.…”

Section: Related Workmentioning

confidence: 99%

Vision-based On-Road Nighttime Vehicle Detection and Tracking Using Taillight and Headlight Features

Parvin¹,

Rozario²,

Islam³

2021

JCC

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An important and challenging aspect of developing an intelligent transportation system is the identification of nighttime vehicles. Most accidents occur at night owing to the absence of night lighting conditions. Vehicle detection has become a vital subject for research to ensure safety and avoid accidents. New vision-based on-road nighttime vehicle detection and tracking system are suggested in this survey paper using taillight and headlight features. Using computer vision and some image processing techniques, the proposed system can identify vehicles based on taillight and headlight features. For vehicle tracking, a centroid tracking algorithm has been used. Euclidean Distance method has been used for measuring the distances between two neighboring objects and tracks the nearest neighbor. In the proposed system two flexible fixed Region of Interest (ROI) have been used, one is the Headlight ROI, and another is the Taillight ROI that could adapt to different resolutions of the images and videos. The achievement of this research work is that the proposed two ROIs can work simultaneously in a frame to identify oncoming and preceding vehicles at night. The segmentation techniques and double thresholding method have been used to extract the red and white components from the scene to identify the vehicle headlights and taillights. To evaluate the capability of the proposed process, two types of datasets have been used. Experimental findings indicate that the performance of the proposed technique is reliable and effective in distinct nighttime environments for detection and tracking of vehicles. The proposed method has been able to detect and track double lights as well as single light such as motorcycle light and achieved average accuracy and average processing time of vehicle detection about 97.22% and 0.01 s per frame respectively.

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“…In fire recognition, the traditional image processing techniques have difficulty in feature extraction, and face a low accuracy [12][13][14][15][16]. Kim and Kim [17] converted the color space format of fire video images, trained the deformation convolution network (DCN) on the image set, and acquired the flame features that adapt to geometric changes, thereby improving the fire recognition effect.…”

Section: Introductionmentioning

confidence: 99%

Extraction and Classification of Image Features for Fire Recognition Based on Convolutional Neural Network

Qi¹,

Liu²

2021

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Fire image monitoring systems are being applied to more and more fields, owing to their large monitoring area. However, the existing image processing-based fire detection technology cannot effectively make real-time fire warning in actual scenes, and the relevant fire recognition algorithms are not robust enough. To solve the problems, this paper tries to extract and classify image features for fire recognition based on convolutional neural network (CNN). Specifically, the authors set up the framework of a fire recognition system based on fire video images (FVIFRS), and extracted both static and dynamic features of flame. To improve the efficiency of image analysis, a Gaussian mixture model was established to extract the features from the fire smoke movement areas. Finally, the CNN was improved to process and classify the fire feature maps of the CNN. The proposed algorithm and model were proved to be feasible and effective through experiments.

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Forest Fire Detection Using a Rule-Based Image Processing Algorithm and Temporal Variation

Cited by 56 publications

References 8 publications

Fire Detection Methods Based on Various Color Spaces and Gaussian Mixture Models

Fire Detection Methods Based on Various Color Spaces and Gaussian Mixture Models

Vision-based On-Road Nighttime Vehicle Detection and Tracking Using Taillight and Headlight Features

Extraction and Classification of Image Features for Fire Recognition Based on Convolutional Neural Network

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