This paper proposes a lightweight remote sensing ship target detection algorithm LR-YOLO based on improved YOLOv5s to meet the lightweight and fast inference requirements of ship target detection tasks involving remote sensing images.

First, the backbone network adopts the ShuffleNet v2 block stacking method, effectively reducing the number of network model parameters and improving the computational speed; second, a region selection module filter is designed to select regions of interest and extract effective features more fully; finally, a circular smooth label is introduced to calculate angle loss and perform rotation detection on remote sensing ship targets, while deformable convolution is used to adapt to geometric deformation and improve detection performance.

The experimental results on the HRSC2016 ship dataset show that the detection accuracy of the algorithm reaches 92.90%, an improvement of 1.3%, with the number of network model parameters only 39.33% that of the baseline model.

The proposed algorithm achieves a balance between lightweight and detection accuracy, providing references for remote sensing ship target detection.