Multi-Scale Remote Sensing Targets Detection with Rotated Feature Pyramid

Yinan Mao, Ziqiang Chen, Hongkun Dou, Danpei Zhao, Ziming Liu

Abstract: For solving the difficult problem of multi-scale and multi-class target detection in complex environments of remote sensing, a target detection network is proposed based on rotated feature pyramid (RFP) and multi-scale context. Proposed method can overcome the interference caused by widely dispersed range in scale and terrain background. By extracting rotated anchors in four feature layers, the RFP module gains ample direction information to enhance plying-up target's contour. Through rotating anchors with a certain angle, RFP can decrease feature information of non-target area and avoid big scale anchor regression. Furthermore, we construct an anchor optimization method using multi-scale context which adjusts the anchor size proportion between different scales to improve the anchor selection accuracy. Experimental results on DIOR dataset demonstrate that the proposed network outperforms six state-of-the-art methods with 4.2% average precision higher. Beyond applicable to different backbones, our network has better performance for multi-class remote sensing targets.