Object Detection Method with Multi-scale Feature Fusion for Remote Sensing Images

Abstract

Object detection for remote sensing images is an important research direction in the field of computer vision,which is widely used in military and civil fields.The objects in remote sensing images have the characteristics of multiple scales,dense arrangement and similarity between classes,so that the object detection methods used in natural images have many omissions and false detection in remote sensing images.To address this problem,this paper proposes an object detection method with multi-scale feature fusion based on YOLOv5 for remote sensing images.Firstly,a residual unit fusing multi-head self-attention is introduced into the backbone network,through which multi-level feature information is fully extracted and semantic differences among diffe-rent scales were reduced.Secondly,a feature pyramid network fusing lightweight upsampling operators is introduced for obtaining high level semantic features and low-level detail ones.And the feature maps with richer feature information could be acquired by feature fusion,which improves the feature resolution of objects at different scales.The performance of the proposed method is evaluated on the datasets DOTA and NWPU VHR-10,and the accuracy(mAP) of the method isimproved by 1.5% and 2.0%,respectively,compared with the baseline model.