scholarly journals A Robust and Dynamic Fire Detection Algorithm using Convolutional Neural Network

2021 ◽  
Vol 1 (2) ◽  
pp. 6-10
Author(s):  
Sivasankar K. ◽  
Shubham Singh ◽  
Kanhaiya Kumar ◽  
Aman Dubey
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Major Part ◽  
Fire Detection ◽  
Data Preprocessing ◽  
Detection Algorithm ◽  
Direction Vector ◽  
Smoke Detection ◽  
Major Process

The major part of the underlying idea is going to detect the fire from upcoming smoke and the shade color of the smoke using convolutional neural network. The fire detection followed by the smoke detection is going to depend on the shade and the direction vector analysis in this paper. Image processing from the available set of data is very vague ideation so in order to strengthen the idea we are incorporating two main features that is the smoke shade and direction vector. For this major process we will involve data preprocessing through bi-variate hypothesis to select two variables as the color of smoke and the direction of the smoke and hence do the further analysis on other features that how are they going to help in the upcoming detection neurons for the robust algorithm of fire detection

scholarly journals A Robust and Dynamic Fire Detection Algorithm using Convolutional Neural Network

2021 ◽  
pp. 6-10
Author(s):  
Mrs. K. Sivasankari ◽  
◽  
Shubham Singh ◽  
Kanhaiya Kumar ◽  
Aman Dubey ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Major Part ◽  
Fire Detection ◽  
Data Preprocessing ◽  
Detection Algorithm ◽  
Direction Vector ◽  
Smoke Detection ◽  
Major Process

The major part of the underlying idea is going to detect the fire from upcoming smoke and the shade color of the smoke using convolutional neural network. The fire detection followed by the smoke detection is going to depend on the shade and the direction vector analysis in this paper. Image processing from the available set of data is very vague ideation so in order to strengthen the idea we are incorporating two main features that is the smoke shade and direction vector. For this major process we will involve data preprocessing through bi-variate hypothesis to select two variables as the color of smoke and the direction of the smoke and hence do the further analysis on other features that how are they going to help in the upcoming detection neurons for the robust algorithm of fire detection.

An efficient fire detection algorithm based on multi‐scale convolutional neural network

2021 ◽  
Author(s):  
Yanying Cheng ◽  
Ke Chen ◽  
Hui Bai ◽  
Chunjie Mou ◽  
Yuchun Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Fire Detection ◽  
Detection Algorithm ◽  
Multi Scale

scholarly journals A Study on Flame and Smoke Detection Algorithm Using Convolutional Neural Network Based on Deep Learning

2020 ◽  
Vol 20 (1) ◽  
pp. 223-232 ◽  
Author(s):  
Jinkyu Ryu ◽  
Dongkurl Kwak
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Region Of Interest ◽  
Detection Algorithm ◽  
Smoke Detection ◽  
Conventional Methods ◽  
Learning Data

Recently, cases of large-scale fires, such as those at Jecheon Sports Center in 2017 and Miryang Sejong Hospital in 2018, have been increasing. We require more advanced techniques than the existing approaches to better detect fires and avoid these situations. In this study, a procedure for the detection of fire in a region of interest in an image is presented using image pre-processing and the application of a convolutional neural network based on deep-learning. Data training based on the haze dataset is included in the process so that the generation of indoor haze smoke, which is difficult to recognize using conventional methods, is also detected along with flames and smoke. The results indicated that fires in images can be identified with an accuracy of 92.3% and a precision of 93.5%.

scholarly journals A real-time video smoke detection algorithm based on Kalman filter and CNN

2021 ◽  
Author(s):  
Alessio Gagliardi ◽  
Francesco de Gioia ◽  
Sergio Saponara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Moving Objects ◽  
Time Window ◽  
Hybrid Approach ◽  
Detection Algorithm ◽  
Regions Of Interest ◽  
Raspberry Pi ◽  
Smoke Detection

AbstractSmoke detection represents a critical task for avoiding large scale fire disaster in industrial environment and cities. Including intelligent video-based techniques in existing camera infrastructure enables faster response time if compared to traditional analog smoke detectors. In this work presents a hybrid approach to assess the rapid and precise identification of smoke in a video sequence. The algorithm combines a traditional feature detector based on Kalman filtering and motion detection, and a lightweight shallow convolutional neural network. This technique allows the automatic selection of specific regions of interest within the image by the generation of bounding boxes for gray colored moving objects. In the final step the convolutional neural network verifies the actual presence of smoke in the proposed regions of interest. The algorithm provides also an alarm generator that can trigger an alarm signal if the smoke is persistent in a time window of 3 s. The proposed technique has been compared to the state of the art methods available in literature by using several videos of public and non-public dataset showing an improvement in the metrics. Finally, we developed a portable solution for embedded systems and evaluated its performance for the Raspberry Pi 3 and the Nvidia Jetson Nano.

scholarly journals Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 816
Author(s):  
Pingping Liu ◽  
Xiaokang Yang ◽  
Baixin Jin ◽  
Qiuzhan Zhou
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Network Model ◽  
Rapid Development ◽  
Image Data ◽  
Lesion Detection ◽  
Great Success

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network’s ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency.

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