Remote sensing image location based on improved Yolov7 target detection

Target detection, as a core issue in the field of computer vision, is widely applied in many key areas such as face recognition, license plate recognition, security protection, and driverless driving. Although its detection speed and accuracy continue to break records, there are still many challenges and difficulties in target detection of remote sensing images, which require further in-depth research and exploration. Remote sensing images can be regarded as a "three-dimensional data cube", with more complex background information, dense and small object targets, and more severe weather interference factors. These factors lead to large positioning errors and low detection accuracy in the target detection process of remote sensing images. An improved YOLOv7 object detection model is proposed to address the problem of high false negative rate for dense and small objects in remote sensing images. Firstly, the GAM attention mechanism is introduced, and a global scheduling mechanism is proposed to improve the performance of deep neural networks by reducing information reduction and expanding global interaction representations, thus enhancing the network's sensitivity to targets. Secondly, the loss function CIoU in the original Yolov7 network model is replaced by SIoU, aiming to optimize the loss function, reduce losses, and improve the generalization of the network. Finally, the model is tested on the public available RSOD remote sensing dataset, and its generalization is verified on the Okahublot FloW-Img sub-dataset. The results showed that the accuracy (MAP@0.5) of detecting objects improved by 1.7 percentage points and 1.5 percentage points respectively for the improved Yolov7 network model compared to the original model, effectively improves the accuracy of detecting small targets in remote sensing images and solves the problem of leakage detection of small targets in remote sensing images.