A Novel Image Intelligent System Architecture for Fire Proof Robot

Madhevan, B.; Ramanathan, Sakkaravarthi; Jha, Durgesh K.

doi:10.1007/978-981-10-3174-8_67

Cited by 2 publications

(1 citation statement)

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“…Fire, as a frequent and highly damaging disaster, is very difficult to prevent. The early detection of fire has long been a focus among domestic and foreign scholars [1][2][3][4][5]. Depending on the detection method, fire detector can be categorized into two types: traditional detectors using sensors, and fire image monitoring systems based on image processing and pattern recognition [6,7].…”

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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