Abstract
Optical remote sensing images have unique advantages in the marine environment, but traditional target detection algorithms have limited performance due to complex background, small target size and dense target distribution. In this study, we propose the YOLOv5–BSD algorithm based on YOLOv5, which optimises the feature fusion network and combines a weighted feature pyramid network and a novel detection head to improve the detection accuracy. The SIoU loss function with angular penalty is introduced to reduce the degree of freedom, and deformable convolution is used to improve the generalization ability. Experiments on the ShipRSImageNet V1.0 data set show that YOLOv5–BSD improves the mAP@0.5, mAP@0.5:0.95, precision and recall by 2.5, 3.7, 2.48 and 2.15 percentage points, respectively, which verifies the effectiveness of the optimization strategy.