Multi-scale fire target identification method and system
A target recognition and multi-scale technology, applied in neural learning methods, character and pattern recognition, instruments, etc., can solve the problems of YOLOv4 with large parameters and inconvenient embedded equipment applications, etc., to achieve simplified parameters, good recognition rate, The effect of fast recognition speed
Pending Publication Date: 2021-06-22
SHANGHAI INST OF TECH
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However, in the actual application of fire, due to the large number of parameters of YOLOv4, it is still not convenient to apply on embedded devices
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[0065] The present invention will be described in detail below in conjunction with specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention. These all belong to the protection scope of the present invention.
[0066] figure 1 It is a flow chart of the steps of the multi-scale fire target recognition method in the embodiment of the present invention, such as figure 1 As shown, the multi-scale fire target recognition method provided by the present invention includes the following steps:
[0067] Step S1: Obtain a training set, which is generated by labeling fire images;
[0068] In the embodiment of the present invention, the step S1 includes steps such as:
[0069] Step S101: Collect, shoot and organi...
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The invention provides a multi-scale fire target identification method and system, and the method comprises the following steps: obtaining a training set which is generated through marking a fire image; carrying out clustering according to the training set, so that a plurality of prior frames are generated; constructing a target detection model and setting the parameters of the target detection model at least according to the prior frames, wherein the target detection model is generated by replacing a CSPDarknet53 trunk feature extraction network in a YOLOv4 algorithm with a MobileNet network; training the target detection model through the training set to generate a fire detection model; and performing fire detection on the input video or image through the fire detection model, and judging whether a fire alarm is sent or not. The parameter quantity of the fire detection model is greatly simplified, the requirements of embedded equipment and real-time performance can be met, and the recognition speed is higher for video fire target detection.
Description
technical field [0001] The invention relates to the field of artificial intelligence image processing, in particular to a multi-scale fire target recognition method and system. Background technique [0002] Fire plays a pivotal role in the long river of our human development. It not only burns out the history of drinking blood, but also ignites the glory of modern society. But fire is a double-edged sword. It can bring warmth and light, but it can also cause painful separation. With the continuous progress of society, the expansion of urban scale and the increase of population density, fire has become one of the main disasters that most frequently and generally threaten public safety and social development. In recent years, many fire incidents have occurred in our country. According to the national fire situation in 2019 released by the National Fire Protection Bureau: a total of 233,000 fires were reported throughout the year, 1,335 people were killed, 837 people were inj...
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IPC IPC(8): G06K9/00G06K9/44G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/04G06N3/082G06V20/10G06V10/34G06V10/40G06V2201/07G06F18/23213G06F18/214
Inventor 黄季兵邓菲
Owner SHANGHAI INST OF TECH