基于Ghost-YOLOv5s的SAR图像舰船目标检测OA北大核心CSTPCD

In the ship target detection based on spaceborne synthetic aperture radar(SAR)images,in order to balance the model size and detection accuracy,a ship target detection method Ghost-YOLOv5s based on SAR images of Ghost convolution is proposed.First,Ghost convolution is introduced into the neck of YOLOv5s to reduce the model parameters and to compress model volume.Then,an ef-ficient channel attention mechanism(ECA)is integrated into the C3 module of the neck to highlight the important features so as to maintain high detection performance.Finally,the SIoU loss function is used to replace the original CIoU loss function,so as to reduce the deviation between the predicted box and the real box and to improve the accuracy of the detection algorithm.The experimental results show that compared with YOLOv5s on SSDD remote sensing data set,the improved model reduces the number of model parameters by 6.28%,the model volume by 6.21%,and the detection accuracy reaches 98.21%,achieving the balance between model size and detection accuracy.