Remote Sensing Image Target Detection Algorithm Based On Residual Shrinkage Network And Attention Mechanism

Remote sensing image target detection is an important part of remote sensing image analysis and application.It can be applied to military strike,urban planning,sea monitoring and other fields.At present,the algorithm based on deep learning is the mainstream of natural image target detection,However,due to the particularity of remote sensing images,the accuracy of deep learning target detection algorithm used in remote sensing images decreases,and the parameters of the model are large,so it is difficult to deploy lightweight.According to the above problems,the main work of the paper is as follows:(1)To address the problem of poor detection accuracy caused by the specificity of remote sensing images,a target detection algorithm based on residual shrinkage network for remote sensing images is proposed.The algorithm adopts the residual shrinkage network as the feature extraction network to reduce the influence of useless background information on the detection effect;in addition to the conventional remote sensing image enhancement methods such as cropping and rotation,the Mosaic image enhancement method is added to enhance the detection effect of small targets;the spatial pyramid pooling combining the maximum pooling and mean pooling is designed to fully fuse the features and combine with the channel attention mechanism to filter the effective features and enhance the detection effect.mechanism to filter the effective features and enhance the detection effect of the algorithm model for rotating targets and multi-scale targets;the CIOU loss is used to optimize the target candidate area for more accurate localization and improve the detection effect for densely arranged targets.It is experimentally demonstrated that the overall mAP of the improved algorithm is improved from 89.2%to 92.2%compared with the original algorithm,and better performance is obtained.(2)To address the problem that deep learning target detection models have large number of parameters and complex models,which are difficult to be deployed in UAVs and other devices in a lightweight manner,we propose a lightweight remote sensing image target detection algorithm combining hybrid attention mechanism.The algorithm constructs ashallow lightweight network model to minimize teh number of parameters and improve the detection speed,To maintain the balance of accuracy and speed,the downsampling module is improved and a feature fusion module with null convolution is used and combined with a hybrid attention mechanism.Meanwhile,for predicting the target bounding box,Kmeans clustering is used for pre-precise adjustment to reduce the loss of accuracy while reducing the number of parameters.Experiments prove that the model file size of the improved algorithm is only 3.5M,while the detection speed can reach 0.022s and mAP can reach 82.9%,which can still guarantee the accuracy and real-time performance while lightweighting the model.