Remote sensing image object detection based on improved RRPN model
The background of the remote sensing object is complex.In addition,the remote sensing object is susceptible to external environment,so the traditional methods fail to meet the requirements of high precision and real-time detection in complex scenes.In view of this,the paper proposes a remote sensing image object detection method based on the improved RRPN model.The framework of feature pyramid network(FPN)is introduced into the residual network of the model,which enabled the effective fusion of high-and low-level features of remote sensing images.The convolutional block attention mechanism(CBAM)combining channel and space is incorporated into the feature extraction network,so as to improve the cross-channel and spatial processing capability of the model in the feature extraction of remote sensing image object.In addition,the original NMS(non-maximum suppression)algorithm is optimized into DIoU-NMS algorithm for eliminating overlapping object frames,and the overlap,distance,scale and other factors among the candidate frames of remote sensing images are taken into account comprehensively,so as to make the regression of object frames more stable.In the comparative and ablation experiments,it is shown that the proposed method achieves mAP(mean average precision)of 77.30%and 90.24%on the public datasets DOTA and HRSC2016,respectively,which are 8.29%and 11.16%higher than that of the original RRPN(rotation region proposal network)model,and it is better than that of the other advanced classical models.This indicates that the proposed method is reasonable and effective for the object detection of remote sensing images in complex environments.
万方数据
维普
