Performance and Comparison Analysis of Image Processing Based Forest Fire Detection

Singh, Lucky; Kumar, Ashok; Priyadarshi, Rahul

doi:10.1007/978-981-15-2854-5_41

Cited by 12 publications

(1 citation statement)

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“…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. Considering the difference between fire source and interference source, Singh et al [18] updated the detection algorithm for the aircraft fire detection system, established a recurrent neural network (RNN) based on long short-term memory (LSTM) and realtime dynamic information, and connected the measured signals into a time series of features to train the established network. Sayyed et al [19] extracted multiple features of the flame, including chroma, area change rate, circularity, number of sharp corners, and centroid displacement; After segmenting the fire images, they created a fast fire recognition model based on time smoothing and logarithmic regression, and tested the model on self-made flame videos.…”

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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Section: Introductionmentioning

confidence: 99%