An intelligent wildfire identification method based on weighted boxes fusion and convolutional block attention module

Abstract

Identifying wildfires is a key task to ensure timely and effective response and prevent the spread of wildfires. The widely used methods for identifying wildfires based on infrared and satellite remote sensing still have problems such as poor identification accuracy and high deployment costs. To address the high deployment costs, this paper adopted the method of deep learning network identifying visible light images.To address the issue of poor accuracy, this paper proposed an intelligent wildfire identification method based on Weighted Boxes Fusion (WBF) and Convolutional Block Attention Module (CBAM). In order to enhance the feature expression of flames and smoke, this paper adopted two different scale SSD networks combined with CBAM to adjust the feature weights of relevant regions and channels adaptively and uses them as the fusion base model. In order to further improve the performance of the model and integrate two different feature extraction strategies, this paper adopted the WBF method to weigh and fuse the identification results of the base model. The method was evaluated on a self-built dataset. The experimental results showed that the mAP of this method reached 96.19%, which is the optimal performance method compared to existing methods. The model proposed in this paper can effectively detect wildfires from images and has practical application potential in wildfire monitoring and management.