Ship Rotated Bounding Box Target Detection in SAR Images Based on Focusing Diffusion MechanismOA

To address the challenges of synthetic aperture radar(SAR)image ship target detection,such as targets being easily submerged by background noise,large-scale changes of ship targets,and inaccurate localization,the authors propose a method for ship rotating frame target detection in SAR images based on focusing diffusion mechanism named FCFO_NET.Firstly,Fast Non-local Means Denoising(Fastnlmd)is used to pre-process SAR images to eliminate noise and improve image quality.Secondly,the backbone network of YOLOv8 is optimized,and a pure convolutional neural network with ConvNeXtV2 architecture is adopted to effectively deal with the problem of poor detection in complex backgrounds.Next,a Focusing Diffusion Pyramidal Network(FDPN)structure is constructed to efficiently capture and integrate rich information across multiple scales.Finally,the oriented bounding box(OBB)rotating frame target detection head is introduced to locate ship targets more accurately.The experimental results on the SSDD+datasets show that the proposed method achieves a mean average precision(mAP)of 98.9%and an F1 score of 96.7%,which are 2.3 and 3.1 percentage points higher than the baseline model,respectively.