Building Detection in Remote Sensing Images Combining Asymmetric Convolution Groups and Channel Attention
Aiming at the problem of building detection in remote sensing satellite images,this paper proposes a building detection mod-el based on a single-stage regression detector. In the model feature extraction network,the feature extraction operation is performed through asymmetric convolution kernel groups,and the channel attention layer is used to further filter the detailed features of the build-ing targets;in the feature enhancement network,the combination of the feature pyramid and the PAN structure layer is used for fusion enhancement of the feature maps in the network,and finally four feature maps of different scales are generated and sent to the detection end. The experimental results show that the model in this paper is superior to several mainstream deep learning detection models in terms of average accuracy,can accurately detect building targets of multiple sizes and angles in satellite remote sensing images,has good robustness to targets in different environments,and has the level of real-time detection in the experimental environment.
万方数据
维普
