Oriented Object Detection Method Based on Cross-Scale Shift
The large intra class scale differences and high inter class similarity of the target pose challenges to tra-ditional remote sensing target detection.Multi scale information fusion and directed detection methods based on Faste-rR-CNN are effective means to address scale differences and inter class similarity.However,the multi-scale weight fusion strategy neglects the extraction of image semantic features across scales,resulting in low detection accuracy.At the same time,the directed detection method based on FasterR-CNN has low accuracy and slow speed.In response to the above issues,a cross-scale shifted oriented object detection approach was put out.First,the feature pyramid net-work(FPN)was enhancedto fuse the multilayer feature map effectively.Then,the cross-scale shift module(CSM)was added to the network in order to improve the correlation between the multi-scale features in the FPN.Finally,the oriented region proposal network(ORPN)was employed to increase the conversion efficiency of the oriented bounding boxes.The DOTA-v1.5 and HRSC2016 remote sensing datasets were used to validate the methodology described inthiswork.Comparedtothecontrolgroup,themeanaverageprecision(mAP)increasesby3.68and1.32percent,respectively.The detection speed increases by 5.9%when performed on a single 2080ti with 1024×1024 images.
NETL
NSTL
万方数据
