Synthetic aperture radar inshore ship detection based on multivariate feature fusion

We propose an algorithm for detecting inshore ship targets in synthetic aperture radar (SAR) images, named MFF-YOLO (You Only Look Once of multivariate feature fusion). On the basis of YOLOv5, the weakly supervised saliency feature module is constructed to enhance the feature texture under the supervision of weakly supervised labels, extract the multi-scale saliency target features, solve the problem that it is difficult to locate potential target regions in the SAR image with insufficient inshore target features, and improve the recall rate by 3.2%; the land–sea feature module is constructed to extract the land–sea distribution features of the image, suppress the land area features, solve the problem of high false alarm rate caused by land clutter in inshore scenes, and improve the precision rate by 3.2%; construct multivariate feature fusion module to fuse the target features, weakly supervised saliency features and land–sea distribution features in multiple scales; and use adaptive thresholding to suppress irrelevant noise to obtain target features enhanced by multivariate features fusion, which solves the problems of insufficient target available information and irrelevant noise interference in SAR images, improves AP0.5 by 3.3%, and improves F1 measure by 2%. This algorithm achieves a precision of 88.3% on the inshore portion of the high-resolution SAR image dataset, a recall of 77.5%, an AP0.5 of 81.6%, and an F1 of 0.825, demonstrating superior detection performance over other comparative algorithms, with good accuracy and robustness.