Object detection in remote sensing images via multi-feature pyramid network with receptive field block

Object detection in optical remote sensing images (ORSIs) remains a difficult task because ORSIs always have some specific characteristics such as scale-differences between classes, numerous instances in one image and complex background texture. To address these problems, we propose a new Multi-Feature Pyramid Network (MFPNet) with Receptive Field Block (RFB) that integrates both local and global features to detect scattered objects and targets with scale-differences in ORSIs. We build a Multi-Feature Pyramid Module (M-FPM) with two cascaded convolution pyramids as the main structure of MFPNet, which handles object detection of different scales very well. RFB is designed to construct local context information, which makes the network more suitable for the objects detection around complex background. Asymmetric convolution kernel is introduced to RFB to improve the ability of feature attraction by adding nonlinear transformation. Then, a two-step detection network is constructed to combine the M-FPM and RFB to obtain more accurate results. Through a comprehensive evaluation of the experimental results on two publicly available remote sensing datasets Levir and DIOR, we demonstrate that our method outperforms state-of-the-art networks for about 1.3% mAP in Levir dataset and 4.1% mAP in DIOR dataset. Experimental results prove the effectiveness of our method in ORSIs of complex environments.