摘要
Abstract
To address the challenges of complex background interference,multi-scale target detection,and poor detection accuracy in remote sensing image target detection,a remote sensing image target detection algorithm based on improved YOLOv8,named CGF-YO-LOv8,was proposed.Initially,the CPAM dual attention module is integrated after the feature extraction network,enabling the model to extract more comprehensive features and more effectively differentiate between target and non-target areas in complex backgrounds.Subsequently,the GFPN enhances detection accuracy for multi-scale targets by effectively combining feature information of different resolutions through cross-scale feature fusion.Finally,utilizing the FIoU anchor box optimization strategy,which assigns different weights to each feature point,not only improves the precision of matching predicted boxes with true boxes but also significantly enhances localization accuracy.Testing on the RSOD dataset demonstrated that this method achieved an average precision of 98.6%,an increase of 6.8%in mAP compared to the original YOLOv8 algorithm,with a frame rate of 250 frames per second(FPS),achieving real-time detection performance.关键词
遥感图像/目标检测/注意力机制/全局特征融合/锚框优化Key words
Remote sensing images/Object detection/Attention mechanism/Global feature fusion/Anchor optimization分类
信息技术与安全科学
