Research on Multi-Category Target Detection Method of Remote Sensing Image with Light Attention Mechanism
Aiming at the problem that conventional remote sensing image target models are difficult to deploy and run on low-power hardware,this paper proposes a lightweight remote sensing image multi-category target detection model.In different layers of the fea-ture extraction network,the convolution kernel group with SE channel attention module and the convolution kernel group with the bot-tleneck structure are used for feature extraction,and then a multi-scale enhancement network containing channel attention is used to output feature map parameters of three scales for final detection.Using RSOD and Google Earth as data sources,the data set is con-structed,and the training set is enhanced with samples.The experimental results show that the model proposed in this paper can quickly and accurately detect multi-category targets in different environments,and the model after training occupies less memory and has a low number of operating parameters,and can be deployed in low-power hardware terminals to quickly and accurately detect ob-jects in remote sensing images.
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