Video smoke detection and recognition method based on transfer learning

Abstract

The invention discloses a video smoke identification detection method based on transfer learning. The method comprises the steps of generating analog data to enlarge the number of smoke image samples;preprocessing the image data set; constructing a target detection network; using an ImageNet image dataset to pre-training an improved VGG-16 network; training a target detection network on the labeled smoke data set by adopting a transfer learning mode, wherein the feature extraction network part uses improved VGG-16 network pre-training weights to perform feature initialization; and extractinga key frame from the video, inputting the key frame into the model for identification and detection, and if smoke is found, returning coordinate information and positioning an area thereof in the video image. Model performance under the condition of limited smoke data is improved by using a transfer learning technology, and a smoke area in a video image can be automatically identified and positioned.

Description

technical field [0001] The present invention relates to the field of image analysis and recognition and the field of machine learning, in particular to a video smoke recognition method, which belongs to the field of image target detection based on deep learning. Background technique [0002] As a common disaster, fire has the characteristics of rapid spread, large losses, and difficulty in fighting. In 2016, a total of 312,000 fires were reported across the country, causing more than 2,600 casualties and direct property losses of 3.72 billion yuan. Especially forest fire, which occurs in an open and complex space environment like a forest, burns, spreads and expands freely. Once it occurs, it will cause huge losses and serious harm to forest resources, ecosystems and human life. The so-called no smoke does not cause fire. Smoke, as a visible mixture produced during the combustion of substances, is often produced before the open flame is observed. It is an important visual f...

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the shortcomings of low expansibility and high false alarm rate of existing video smoke recognition methods, the present invention proposes a video smoke recognition and detection method based on migration learning with strong generalization ability and high precision, using the improved Faster R- The CNN neural network identifies and locates the smoke area in the video, and at the same time uses transfer learning technology to reduce the training difficulty of the deep neural network. location in the area

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