Improved Ship Detection with YOLOv8 Enhanced with MobileViT and GSConv

Abstract

In tasks that require ship detection and recognition, the irregular shapes of ships and complex backgrounds pose significant challenges. This paper presents an advanced extension of the YOLOv8 model to address these challenges. A lightweight visual transformer, MobileViTSF, is proposed and combined with the YOLOv8 model. To address the loss of semantic information that arises from inconsistent scales in the detection of small ships, a layer intended for the detection of small targets is introduced to lead to improved fusion of deep and shallow features. Furthermore, the traditional convolution (Conv) blocks are replaced with GSConv blocks, and a novel GSC2f block is designed for fewer model parameters and improved detection performance. Experiments on a benchmark dataset suggest that this new model can achieve significantly improved accuracy for ship detection with fewer model parameters and a reduced model size. A comparison with several other state-of-the-art methods shows that higher accuracy can be obtained for ship detection with this model. Moreover, this new model is suitable for edge computing devices, demonstrating practical application value.