改進(jìn)YOLOv8n模型的火災(zāi)場(chǎng)景火焰檢測(cè)方法

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Abstract：Aiming at the problem of low accuracy in flame detection caused by complex smoke and dust environments in fire scenes，an efficient and precise flame detection method based on the YOLOv8n model was proposed.First，a variety of fire scene images were selected as the original images for the dataset，and random noise，such as salt and pepper noise， was added to simulate a smoke and dust environment. Second， a median filtering module was embedded at the front of the model's network framework to enhance the network's capability to handle interference noise in smoke and dust environments.Finall，byutilizing Ghost convolution modules and designing crosslayer connection networks at diferent lay levels，the number of parameters was reduced while the generalization capability of the network was optimized.This enable real-time and high-precision flame detection in fire scene with noise interference. Experimental results show that the improved YOLOv8n model had superior real-time performance and detection accuracy performance.

Keywords：flame detection；random noise；YOLOv8n model；median filtering module； lightweight Ghost convolution

火災(zāi)的發(fā)生對(duì)人們的生命和財(cái)產(chǎn)造成嚴(yán)重威脅[]，，傳統(tǒng)的火災(zāi)檢測(cè)方法采用溫度傳感器進(jìn)行檢測(cè)，這種檢測(cè)方法造價(jià)昂貴且不適用室外場(chǎng)景的應(yīng)用[2]。（剩余12496字）