Research on the Lightweight Forest Fire Video Monitoring Method by MobileNet
The application of video fire detection technology can effectively improve the monitoring and early warning capabilities of forest fires,which is of great significance in ecological protection and social public safety.The static and dynamic feature de-tection methods of images that are more suitable for forest fire detection have been studied.At the same time,it makes the con-volutional neural network YOLO lightweight,and GAM is introduced to balance accuracy,model size and speed.The opti-mized model reduces the number of parameters by approximately 80％at the expense of 1.9％decrease in accuracy,and the ac-curacy reaches 92.4％on the self-made forest fire dataset.This new fire monitoring method based on the combination of color,motion and machine learning technology is lightweight and streamlined,and has reference value for the design of real-time fire monitoring system.
video fire detectioncolor modelmotion detectionconvolutional neural networkglobal attention mechanism
万方数据
维普
