In the field of UAV smoke detection

due to the significant variations in different detection scenes

existing smoke detection algorithms often suffer from issues such as low detection accuracy and slow speed. To address these issues

in this paper we constructed a new UAV smoke dataset (USD) in multiple scenes

and proposed an improved YOLOx UAV smoke detection algorithm in multiple scenes. Firstly

we introduced an improved attention module into the YOLOx network to improve the extraction process of channel features and spatial features respectively

which can extract more representational smoke features. Then

we presented a two-way fusion network to enhance the fusion ability of multi-scale feature fusion module for small smoke target features. Finally

we utilized a Focal-EIOU loss function to address the issues such as the imbalance of positive and negative samples in the training process

and the distance and coincidence degree of two frames cannot be reflected when the prediction frame and real frame do not intersect. Experimental results show that the proposed algorithm has good robustness when applied to UAV smoke detection tasks in multiple scenarios. Compared with several classical smoke detection algorithms

the accuracy of the proposed smoke detection method on different data sets has been improved respectively. For instance

compared with the original YOLOx-s model

the accuracy was improved by 2.7%

the recall rate was improved by 3%

and the speed reached 73.6 frames per second.