Remote Sensing Image Rotation Target Detection Algorithm Based on Attention Mechanism

Although strides in target detection, challenges persist in remote sensing image target detection due to complex backgrounds, small targets, and interference from background and other target information within the horizontal bounding box. To enhance remote sensing image target detection, this study introduces a YOLOv7-based attention mechanism for rotation target detection. The network model's loss function is first enhanced to extract rotating target angle information, ensuring accurate detection of the rotation angle. Subsequently, the ELAN module undergoes optimization to augment the model's feature extraction capability in remote sensing images by integrating an improved attention mechanism module and structural re-parameterization concept. Experimental testing on a self-built dataset demonstrates an overall detection accuracy of 92.7% for the proposed method, surpassing other mainstream rotational target detection algorithms in mAP. Results affirm that the proposed approach effectively enhances the rotation detection performance of remote sensing images.