YOLOv5 Improved Smoke Detection Algorithm *

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The impact of fire on natural resources and the ecological environment is reflected in the fact that it alters the global carbon cycle, vegetation renewal process and soil properties through a combination of natural and human factors, thereby aggravating climate change, and also seriously endangers public life and property and causes huge economic losses. In the early stage of fire, smoke is more obvious than flame, so accurate and fast smoke detection algorithm is of great significance for preventing fire. In order to meet the requirements of smoke detection accuracy and detection speed, an improved real-time smoke detection algorithm is proposed on the basis of YOLOv5. Based on the YOLOv5 network structure, the GhostNet lightweight network is used to replace the backbone network, and the prediction header is replaced by C3Ghost. The C3 module only uses a small amount of traditional convolution to generate some feature maps, which greatly reduces the network parameters and speeds up the network inference speed; the CA attention mechanism is added to the backbone network, which makes the mobile network by embedding position information into the channel attention. Get information on a larger area and avoid introducing large overhead. Compared with the original YOLOv5 network model, the average detection accuracy of the improved method is increased by 2.2%, the FPS is changed from 181.82 to 222.22 frames per second, and the number of model parameters is reduced by 51.3%. The experimental results show that this method can effectively improve the performance of the smoke detection model.