Small Target Remote Sensing-Imagery Recognition Algorithm Based on Improved Yolov7
In view of the situation where small targets in remote sensing images are missed or wrongly detected,a small target remote sensing image recognition algorithm based on improved YOLOv7 is proposed.Construct a coordinate convolution stacking branch CCSB(Coordinate Convolutional Stacking Branch)to replace the original ELAN to improve the network's detection ability of dense targets;propose a joint feature extraction module JFEM(Joint feature extraction module)based on shallow features to extract multi-scale information;propose the DRAM(Deep routing attention module)to make the model focus on the key information in the image;a feature fusion strategy FFS(Feature fusion strategy)is proposed based on information fusion to remove conflict informa-tion within the feature pyramid;a mixed loss function MLF(Mixed loss function)is proposed to improve the po-sitioning ability of the target.The results show that the mAP reaches 92.32%on the DIOR remote sensing data set,which is 3.63%higher than the original YOLOv7.The mAP reaches 97.80%on the RSOD data set,which is 3.50%higher than the original YOLOv7,which proves the effectiveness of the improved method on remote sens-ing images.
remote sensing imageYOLOv7feature fusionsmall target detectionloss function
NETL
NSTL
万方数据
