Remote sensing image object detection based on improved YOLOv8

Abstract

Aiming at the issues of low object detection accuracy and false detection of small object in complex background, a modified remote sensing image detection algorithm based on YOLOv8 is proposed. Use of the RFA convolution kernel enhances the extraction of network space features and their associated global information, and further bring in the Deformable Attention mechanism to obtain a more flexible dynamic attention model. The Powerful-IoU loss function is introduced in the regression loss function, which improves the convergence speed of the network and the object detection accuracy. On the RSOD and DOTAv1.0 datasets the mAP of the improved algorithm is increased by 1.5% and 2.3% respectively.