Small object detection in remote sensing images based on improved YOLOV7

Remote sensing images have a wide range of applications in geological exploration, disaster warning, military reconnaissance and other fields, and the detection of specific targets in remote sensing images can improve the efficiency of image analysis. However, due to the high shooting height of remote sensing images, the target pixel area obtained is small, and the complex background of the image is unfavorable for detection. To address this problem, a practical feature extraction network for remote sensing images is designed and transplanted into the YOLO v7 algorithm. Firstly, a multi-scale nested Vision Transformer model is proposed, compared with the standard Vision Transformer model, which can simultaneously compute multiple attention to multiple feature maps at different scales and merge the multi-scale features to enhance the perception of the Vision Transformer model for the global features; secondly, a fission type multi-field convolution model is proposed, which is a multi-field convolution model. fission multi-perceptual field convolution module, which enhances the processing capability of local features by grouping feature maps for computation; finally, a multi-level feature extraction network is designed to combine global and local features in remote sensing maps to enhance the feature richness of the network. The related experimental tests are carried out on three datasets, including RSOD, and the experimental results show that the designed algorithm improves the F1-Score index by 3.65% on average compared with YOLO v7, and improves the mAP index by 3.53% on average compared with YOLO v7.