Image processing based forest fire detection using YCbCr colour model

Premal, C. Emmy; Vinsley, S. S.

doi:10.1109/iccpct.2014.7054883

Cited by 50 publications

(20 citation statements)

References 6 publications

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“…In contrast, false-positive was counted, if the image frame has no fire, and the result was determined as a fire. Table 1 shows Mathematical Problems in Engineering Figure 5: Applying the rules (7)-(11) to input images: (a) original RGB images, (b) binary images using rule (7), (c) binary images using rule (8), (d) binary images using rule (9), (e) binary images using rule (10), (f) binary images using rules (7) through (11).…”

Section: Results and Performance Analysismentioning

confidence: 99%

“…Converting RGB Images to YCbCr. Due to the fact that different kinds of moving objects can be included after applying background subtraction, such as trees, animals, birds, and people, therefore images from the background subtraction stage are converted to YCbCr [9] to select candidate fire regions using (3).…”

Section: Movement Containing Region Detection Based On Background Submentioning

confidence: 99%

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

Mahmoud

Ren

2018

Mathematical Problems in Engineering

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Forest fires represent a real threat to human lives, ecological systems, and infrastructure. Many commercial fire detection sensor systems exist, but all of them are difficult to apply at large open spaces like forests because of their response delay, necessary maintenance needed, high cost, and other problems. In this paper a forest fire detection algorithm is proposed, and it consists of the following stages. Firstly, background subtraction is applied to movement containing region detection. Secondly, converting the segmented moving regions from RGB to YCbCr color space and applying five fire detection rules for separating candidate fire pixels were undertaken. Finally, temporal variation is then employed to differentiate between fire and fire-color objects. The proposed method is tested using data set consisting of 6 videos collected from Internet. The final results show that the proposed method achieves up to 96.63% of true detection rates. These results indicate that the proposed method is accurate and can be used in automatic forest fire-alarm systems.

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Section: Results and Performance Analysismentioning

confidence: 99%

Section: Movement Containing Region Detection Based On Background Submentioning

confidence: 99%

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

Mahmoud

Ren

2018

Mathematical Problems in Engineering

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“…Kemudian semakin rendah komponen Cb maka semakin baik karena Cb mewakili ChrominanceBlue atau tingkat kebiruan citra dan semakin tinggi komponen Cr maka semakin baik karena Cr mewakili ChrominanceRed atau tingkat kebiruan citra. Sehingga untuk didapatkan nilai tresshold untuk masing -masing komponen dapat menggunakan persamaan (6). [7] (19) dimana T merupakan ambang batas atau treshold, adalah rata-rata nilai mean dan adalah ratarata nilai standar deviasi.…”

Section: Metodologi Penelitianunclassified

Deteksi Dini Kebakaran Hutan Dan Lahan Memanfaatkan Ekstraksi Exif Pada Informasi Gambar Berbasis Pengolahan Citra

Wibowo

Teguh

Lestari

2021

JTI

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Forest fire detection system is one of important tools in preventing and mitigating forest and land fires. In Indonesia, the detection of forest and land fires relies on hotspot information captured from satellites. However, the location obtained by the satellite has a horizontal error of 2 km from the ground check data. Therefore, these information are less relevant to the actual location. In this research, an android app is proposed to extract Exchangeable Image Format (EXIF) photo metadata. The metadata has image information such as latitude and longitude, to obtain the location of forest fires reported by the application user. In addition, this research implemented one of the image processing methods to classify fire and smoke in images of fires. Color filtering method is used based on the color space of Red Green Blue (RGB), Hue Saturation Value (HSV) and YCbCr. This classification process aims to ease the burden on the admin in confirming user reports. The results of the fire and smoke classification process are described using a confusion matrix. This matrix produces an accuracy rate of 75%, a precision of 80% and a recall of 80% for a fire classification and an accuracy of 70%, a precision of 92% and a recall of 87% for smoke classification. There are 25% and 30% of misclassified data of fire and smoke. This is because the color filtering method classifies each color pixel from the image, therefore many pixels that are not classified as fire or smoke images are classified because there are other objects that have a range of colors to classify fire and smoke

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“…In [2], it is inferred that Cr>Cb assuming that R>G>B and formed a rule to classify an image pixel as fire or non-fire. In [10] four rules based on YCbCr color model have been proposed to detect fire region in an image. The first two detects low intensity flame regions and the last two rules for high intensity flame regions.…”

Section: Introductionmentioning

confidence: 99%

“…In [11] presents the comparative analysis of five recent vision based fire detection system and a fire detection system based on LUV color space and hybrid transforms is proposed. In this paper, we propose a simple fire detection method based only on the chrominance color of the YCbCr model unlike in [2] and [10] in which both luminance and chrominance components are used to detect fire regions. Here, we increase the variance of the chrominance to make the fire regions more prominent than rest of the image and then segment the fire region through automatic binarization.…”

Section: Introductionmentioning

confidence: 99%

Detection of Fire Regions from a Video Image Frames in YCbCr Color Model

Singh*¹,

Deb²

2019

IJRTE

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Proposed here is a fire region detection method from a recorded video captured during the occurrence of fire. This method is based only on the chrominance components of the YCbCr color model. To distinguish the fire-region in an image frame of fire video containing the fire region, the difference between Cr and Cb is computed. The difference is enhanced by computing the square of it and then normalize range of difference squared to 0 to 255. It is then binarized at using automatic thresholding method to segment the fire region from the non-fire region. The fire region in the binary is located using the connected component analysis and the region is mapped to the original image frames of the fire video. We have tested the method in the actual fire video and it is found that the method can appropriately locate the fire regions in every image frame of the video. The method is simple and fast and hence can be used to forest fire monitoring using drones

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Image processing based forest fire detection using YCbCr colour model

Cited by 50 publications

References 6 publications

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

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

Deteksi Dini Kebakaran Hutan Dan Lahan Memanfaatkan Ekstraksi Exif Pada Informasi Gambar Berbasis Pengolahan Citra

Detection of Fire Regions from a Video Image Frames in YCbCr Color Model

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