Abstract: Timely warning of forest fire plays a crucial role in forest protection. Due to the complex background of forest fireworks and many interference factors, the detection accuracy and efficiency are affected. Therefore, a forest fire image detection method based on CenterNet2 is proposed. The lightweight backbone network VoVNetV2 combined with asymmetric convolution kernel is used to improve the feature extraction ability and detection speed. Meanwhile, an attention mechanism eSE (Effective Squeeze and Extraction) is introduced into the weighted bidirectional feature pyramid network for feature fusion, so as to improve the accuracy of small target detection. Then, SIoU loss function is used to improve the effect of target box regression. The simulation results show that the method can accurately detect forest fire in real time, and the false rate is low. 

Key words: forest fires, asymmetric convolution kernel, attention mechanism, SIoU loss function