Multi-granularity feature enhancement network for maritime ship detection

Abstract

Due to the characteristics of high resolution and rich texture information, visible light images are widely used for maritime ship detection. However, these images are susceptible to sea fog and ships of different sizes, which can result in missed detections and false alarms, ultimately resulting in lower detection accuracy. To address these issues, a novel multi-granularity feature enhancement network, MFENet, which includes a three-way dehazing module (3WDM) and a multi-granularity feature enhancement module (MFEM) is proposed. The 3WDM eliminates sea fog interference by using an image clarity automatic classification algorithm based on three-way decisions and FFA-Net to obtain clear image samples. Additionally, the MFEM improves the accuracy of detecting ships of different sizes by utilising an improved super-resolution reconstruction convolutional neural network to enhance the resolution and semantic representation capability of the feature maps from YOLOv7. Experimental results demonstrate that MFENet surpasses the other 15 competing models in terms of the mean Average Precision metric on two benchmark datasets, achieving 96.28% on the McShips dataset and 97.71% on the SeaShips dataset.