Multi Feature Analysis of Smoke in YUV Color Space for Early Forest Fire Detection

Prema, C. Emmy; Vinsley, S. S.; Suresh, S.

doi:10.1007/s10694-016-0580-8

Cited by 56 publications

(11 citation statements)

References 20 publications

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“…Smoke images show different results in each color space due to the change in illumination and environment [25]. Past research discussed smoke images in different color spaces [26][27][28].…”

Section: Candidate Smoke Region Segmentationmentioning

confidence: 99%

“…The results showed that color information is least affected by illumination changes in the YUV color space. According to the probability statistics of thousands of real smoke images, Prema, Vinsley, and Suresh [25] demonstrated that the U-V of non-smoke regions was mainly distributed from 0 to 40, while candidate smoke regions were mainly distributed between 40 and 130. The segmentation strategy is shown in the following formula.…”

Section: Candidate Smoke Region Segmentationmentioning

confidence: 99%

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Detection of Wildfire Smoke Images Based on a Densely Dilated Convolutional Network

Zhao

Zhang

et al. 2019

Electronics

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Recently, many researchers have attempted to use convolutional neural networks (CNNs) for wildfire smoke detection. However, the application of CNNs in wildfire smoke detection still faces several issues, e.g., the high false-alarm rate of detection and the imbalance of training data. To address these issues, we propose a novel framework integrating conventional methods into CNN for wildfire smoke detection, which consisted of a candidate smoke region segmentation strategy and an advanced network architecture, namely wildfire smoke dilated DenseNet (WSDD-Net). Candidate smoke region segmentation removed the complex backgrounds of the wildfire smoke images. The proposed WSDD-Net achieved multi-scale feature extraction by combining dilated convolutions with dense block. In order to solve the problem of the dataset imbalance, an improved cross entropy loss function, namely balanced cross entropy (BCE), was used instead of the original cross entropy loss function in the training process. The proposed WSDD-Net was evaluated according to two smoke datasets, i.e., WS and Yuan, and achieved a high AR (99.20%) and a low FAR (0.24%). The experimental results demonstrated that the proposed framework had better detection capabilities under different negative sample interferences.

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Section: Candidate Smoke Region Segmentationmentioning

confidence: 99%

Section: Candidate Smoke Region Segmentationmentioning

confidence: 99%

Detection of Wildfire Smoke Images Based on a Densely Dilated Convolutional Network

Zhao

Zhang

et al. 2019

Electronics

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“…Currently, there are many algorithms for detecting forest fires [7][8][9]. It should be noted that at present, a high quality of detection of research objects based on machine learning has been achieved, in particular, based on the "Object detection" technology [10,11].…”

Section: Introductionmentioning

confidence: 99%

Integrating Traditional Machine Learning and Neural Networks for Image Processing

Laptev

Gerget

et al. 2021

Proceedings of the 31th International Conference on Computer Graphics and Vision. Volume 2

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The article describes a feasibility study to assess the use of neural networks and traditional machine learning algorithms to solve various problems including image processing. A brief description of some algorithms of traditional machine learning, as well as anautomated service for choosing the best method for a specific task, is given. The authors also describe the features of artificial neural networks and the most popular places for theirapplication. An algorithm for solving the problem of detecting fire hazardous objects andlocalizing a fire source in a forest using video sequence frames is presented. The article compares the characteristics of artificial neural network models according to the followingcriteria: underlying architecture, the number of analyzed frames, the size of the input image, the transfer learning model used as a feature vector composing network. Acomparative analysis of traditional machine learning algorithms and neural networks withlong short-term memory in the problem of classification of forest fire hazards is made. A solution to localization of the source of fire based on clustering is described. A hybrid algorithm for finding a fire source in a forest is developed and illustrated.

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“…Forest fire smoke segmentation outputs a mask with detailed edges, involving object classification, localization and boundary delineation. Traditional forest fire smoke segmentation methods mainly use handcrafted features, such as forest fire smoke color, texture and motion [21][22][23][24][25][26][27][28][29][30][31]. Nevertheless, it is pretty difficult to define, design or choose useful features due to large variations of forest fire smoke appearance, resulting in quite poor segmentation performance.…”

Section: Introductionmentioning

confidence: 99%

A Semantic Segmentation Method for Early Forest Fire Smoke Based on Concentration Weighting

Wang

Zheng

Yin³

et al. 2021

Electronics

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Forest fire smoke detection based on deep learning has been widely studied. Labeling the smoke image is a necessity when building datasets of target detection and semantic segmentation. The uncertainty in labeling the forest fire smoke pixels caused by the non-uniform diffusion of smoke particles will affect the recognition accuracy of the deep learning model. To overcome the labeling ambiguity, the weighted idea was proposed in this paper for the first time. First, the pixel-concentration relationship between the gray value and the concentration of forest fire smoke pixels in the image was established. Second, the loss function of the semantic segmentation method based on concentration weighting was built and improved; thus, the network could pay attention to the smoke pixels differently, an effort to better segment smoke by weighting the loss calculation of smoke pixels. Finally, based on the established forest fire smoke dataset, selection of the optimum weighted factors was made through experiments. mIoU based on the weighted method increased by 1.52% than the unweighted method. The weighted method cannot only be applied to the semantic segmentation and target detection of forest fire smoke, but also has a certain significance to other dispersive target recognition.

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Multi Feature Analysis of Smoke in YUV Color Space for Early Forest Fire Detection

Cited by 56 publications

References 20 publications

Detection of Wildfire Smoke Images Based on a Densely Dilated Convolutional Network

Detection of Wildfire Smoke Images Based on a Densely Dilated Convolutional Network

Integrating Traditional Machine Learning and Neural Networks for Image Processing

A Semantic Segmentation Method for Early Forest Fire Smoke Based on Concentration Weighting

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