Abstract: Remote sensing image target detection has a wide range of applications in fields such as land resource survey,disaster monitoring,and military reconnaissance.In response to the difficulty of SSD(Single Shot MultiBox Detector) models in effectively extracting features of small targets during remote sensing image target detection,which is detrimental to the detection of small targets,this paper proposes a remote sensing image target detection model based on reparameterization and multi-scale feature fusion,named RMSFF-SSD(Reparameterization Multi-Scale Feature Fusion SSD).This model is an improvement based on the SSD model.Firstly,the convolutional layers in the backbone feature extraction network of SSD are replaced with convolutions that have reparameterization properties to extract features,and at the same time,the SE attention mechanism is introduced into the reparameterized convolutions to capture the dependencies between channels and suppress useless features.Secondly,the features extracted by the feature extraction network are fused through multi-level feature fusion to integrate global information and local detail information,further enhancing the target features.Finally,the six different scales of feature maps obtained after fusion are used for target detection.The experimental results of target detection on the NWPU VHR-10 dataset show that the average precision of the proposed RMSFF-SSD512 target detection model is 89.7%,which is significantly higher than the DSSD(78.7%) model,FSSD(86.7%) model,FPN(68.9%) model,Faster R-CNN(44.2%) model,and YOLOv5(83.7%) model.

Key words: Reparameterization, Feature fusion, Remote sensing detection, SSD, SE attention mechanism