Image-type fire detection method based on cumulative prospect image

Abstract

The invention provides an image-type fire detection method based on a cumulative prospect image, builds the cumulative prospect image, and is used for real-time detection of a fire image. The method comprises the following steps of: extracting the cumulative prospect image obtained by a monitoring camera through a computer; blocking the image; counting brightness values of all pixels of each image block in the cumulative prospect image; judging according to preset sensitivity; and sending out a warning signal if a fire is detected, otherwise returning to the initial steps to continuously process next frame image. According to the method, the definition of historical motion images is changed to obtain the cumulative prospect image, and the cumulative prospect image can be used for reflecting active characteristics of fire flame better and greatly reducing false alarm rate and has good anti-noise capability. Moreover, the method has the advantages of simpler arithmetic principle, low computation and quite good real-time performance and can meet the requirement of the conventional image-type fire detection technology for the real-time performance.

Description

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AI Technical Summary

Benefits of technology

This patented technique describes an improved way to detect fires accurately with moving backgrounds like video or still photos without being affected by external factors such as sunlight. By comparing this data against previous scenes during training periods, we can identify areas where there are no significant changes over longer times compared to usual conditions. These techniques help reduce costs associated with monitoring fires while minimizing damage caused due to environmental variables including temperature variations.

Problems solved by technology

Technological Problem addressed in this patents relates to improving image quality and identifying fires efficiently when detecting objects undergoing visual imagery. Current methods involve complicated calculations involving multiple steps like calculating histogram values and performing convolution operations, but these techniques may result in errors caused by factors including ambient brightness changes, lamps shading effects, and noise. Therefore, there needs improvements over previous approaches to address these issues.

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