LSDNet: a lightweight ship detection network with improved YOLOv7

Abstract

Accurate ship detection is critical for maritime transportation security. Current deep learning-based object detection algorithms have made marked progress in detection accuracy. However, these models are too heavy to be applied in mobile or embedded devices with limited resources. Thus, this paper proposes a lightweight convolutional neural network shortened as LSDNet for mobile ship detection. In the proposed model, we introduce Partial Convolution into YOLOv7-tiny to reduce its parameter and computational complexity. Meanwhile, GhostConv is introduced to further achieve lightweight structure and improve detection performance. In addition, we use Mosaic-9 data-augmentation method to enhance the robustness of the model. We compared the proposed LSDNet with other approaches on a publicly available ship dataset, SeaShips7000. The experimental results show that LSDNet achieves higher accuracy than other models with less computational cost and parameters. The test results also suggest that the proposed model can meet the requirements of real-time applications.