FAGNet: Multi-Scale Object Detection Method in Remote Sensing Images by Combining MAFPN and GVR

Abstract

<p indent=0mm>Remote sensing images of large scenes are complex, and have the characteristics of many categories of objects, different scales and changeable directions, which lead to the problem of multi-class, multi-scale and multi-oriented of objects in remote sensing images. A remote sensing image object detection method based on multi-scale attention feature pyramid network (MAFPN) and gliding vertex regression (GVR) mechanism is proposed. Firstly, multi-layer feature maps are extracted from backbone network as input of MAFPN, which combines feature fusion and attention mechanism. On the basis of fusing feature maps of multi-scale, channel attention and spatial attention mechanisms are used to suppress noise, enhance effective feature reuse, and improve the network’s adaptability to object multi-scale features. The fusion feature map output by MAFPN is input to the region proposal network to generate the regions of interest, and then they will be sent to the classification regression network. The GVR mechanism is used in the object classification regression network and the four vertex offset ratio parameters and rotation factors are added on the basis of predicting the horizontal boxes, which converts the horizontal boxes into a rotating box to reduce the redundant area in the bounding boxes, makes the predicted rotating bounding boxes fit the object more closely. The experimental results on the DOTA public dataset, compared with many classical detection algorithms based on convolutional neural networks, show that the average detection accuracy of the proposed method is significantly improved, which can detect objects of multi-scales and multi-oriented more accurately, and achieve the robust detection of multi-scale objects.