Fire Detection Using Image Processing Techniques with Convolutional Neural Networks

Sadewa, Raam Pujangga; Irawan, Budhi; Setianingsih, Casi

doi:10.1109/isriti48646.2019.9034642

Cited by 25 publications

(11 citation statements)

References 1 publication

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“…The following are the test results based on the camera angle when shooting. The results of the CNN Algorithm for image processing cases get pretty good results like previous research that uses this algorithm also [6] - [8].…”

Section: Methodssupporting

confidence: 54%

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Cholesterol Detection Based on Eyelid Recognition Using Convolutional Neural Network Method

Pratama

Novianty

Setianingsih

2020

EECSI

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Lack of public awareness of health will cause serious problems. A small example, people now tend to always consume fatty foods without thinking about the risk of cholesterol levels in the body. Information on the level of cholesterol suffered by humans can be seen on the human eyelids. The eyelids, one part of the eye, can be known as a person's cholesterol level by observing the eyelids' shape and condition, but many people do not know about this. This application is an application made to detect cholesterol based on the shape of the eyelids. This can determine whether a person is exposed to cholesterol or not, using the Convolutional Neural Network (CNN) method in the classification process. This study provides an output in the form of early detection of cholesterol and prevention so that users can minimize the possibility of illness that will be suffered. This research was conducted to detect cholesterol one eyelid based on digital images. For detecting a cholesterol level, this system got 95.83% of accuracy.

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Section: Methodssupporting

confidence: 54%

“…Convolutional Neural Networks are one type of Deep Learning, often used to classify data such as image, sound, text, etc [6] - [8].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Cholesterol Detection Based on Eyelid Recognition Using Convolutional Neural Network Method

Pratama

Novianty

Setianingsih

2020

EECSI

Self Cite

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“…They conclude the paper that the usage of SVM ensures 93.52% classification success. In [9], Sadewa et al propose to reduce the time elapsed from the start of the fire to the shortest time so that smoke sensors can test smoke indoors. For this purpose, the data received by the web cam placed at a corner point of the room instead of the smoke sensor is gathered.…”

Section: Related Workmentioning

confidence: 99%

“…Deep learning models are employed for the purpose of ensuring automatic feature extraction by training complex features to acquire more informative demonstration of data. In addition to this, deep learning methods are also utilized employed for the classification tasks in many areas [8][9][10]. Convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks (LSTMs), generative adversarial Networks (GANs), radial basis function networks (RBFs), and deep belief networks (DBNs) are widely used and well-known architectures.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Analysis of Forest Fire Detection using Deep Learning Models and Conventional Machine Learning Algorithms

Kukuk

Kilimci

2021

International Journal of Computational and Experimental Science and Engineering

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Forest fire detection is a very challenging problem in the field of object detection. Fire detection-based image analysis have advantages such as usage on wide open areas, the possibility for operator to visually confirm presence, intensity and the size of the hazards, lower cost for installation and further exploitation. To overcome the problem of fire detection in outdoors, deep learning and conventional machine learning based computer vision techniques are employed to determine the fire detection when indoor fire detection systems are not capable. In this work, we propose a comprehensive analysis of forest fire detection using conventional machine learning algorithms, object detection techniques, deep and hybrid deep learning models. The contribution of this work to the literature is to analyze different classification and object detection techniques in more details that is not addressed before in order to detect forest fire. Experiment results demonstrate that convolutional neural networks outperform other methods with 99.32% of accuracy result.

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“…In summary, the existing image processing-based fire detection techniques have been primarily applied to identify suspected regions and fit the flame foreground contours. Despite their high recognition accuracy, these techniques cannot realize real-time warning of fires in actual scenes, and their recognition algorithms are not robust enough [21][22][23][24][25][26][27]. To solve the problems, this paper tries to extract and classify image features for fire recognition based on the CNN.…”

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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Fire Detection Using Image Processing Techniques with Convolutional Neural Networks

Cited by 25 publications

References 1 publication

Cholesterol Detection Based on Eyelid Recognition Using Convolutional Neural Network Method

Cholesterol Detection Based on Eyelid Recognition Using Convolutional Neural Network Method

Comprehensive Analysis of Forest Fire Detection using Deep Learning Models and Conventional Machine Learning Algorithms

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

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