A smoke detection method based on multi-network model fusion

Abstract

The invention relates to a smoke detection method based on multi-network model fusion, and two network models of VGG16 and ResNet50 are fused to realize reliable detection of smoke. According to the fusion network provided by the invention, more abundant smoke image detail features can be extracted, and the distinguishing capability of the features on the smoke image and the smoke-like image is enhanced. By adopting a feature transfer learning method based on an isomorphic space, feature extraction layers of pre-trained VGG16 and ResNet50 models can be well migrated to a target data set classification task in a smoke scene, and meanwhile, the generalization capability of the model is improved. By fusing the multi-network model, the distinguishing capability of the characteristics is enhanced, the false alarm phenomenon caused by targets such as cloud and fog similar to smoke is reduced, and the reliability of smoke detection is further improved.

Description

technical field [0001] The invention relates to a smoke detection method for multi-network model fusion. Background technique [0002] Fire early warning technology based on computer vision is playing an increasingly important role in the field of fire detection and early warning. Among them, smoke detection is of great significance to the early detection of fire. At present, smoke detection methods are mainly divided into two categories. One is to realize fire detection based on traditional features such as color, shape, texture, and motion. For example, the document "Smoke detection invideo using wavelets and support vector machines" uses discrete wavelet transform to extract smoke image features , and apply a support vector machine for classification. The document "Smoke Detection in Video Sequences: Combined Approach" uses the method of area matching to extract the suspected smoke area, and uses the color change speed of the background object to identify the smoke. Th...

AI Technical Summary

Problems solved by technology

The document "Smoke Recognition Method Based on Deep Transfer Learning" uses the VGG16 network for smoke recognition, which can distinguish five scenes such as smoke and flames, and further improves the accuracy of smoke detection, but the VGG16 network block uses the largest pool It will lose part of the features of the original image, and it is still difficult to distinguish smoke-like targets such as smoke and clouds.

In general, although deep learning methods can greatly improve the performance of smoke detection, the existing methods are still not capable of distinguishing objects that are particularly similar to smoke, such as clouds and fog, and the false alarm rate is still high.

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