FE-YOLO: YOLO ship detection algorithm based on feature fusion and feature enhancement

The technology for detecting maritime targets is crucial for realizing ship intelligence. However, traditional detection algorithms are not ideal due to the diversity of marine targets and complex background environments. Therefore, we choose YOLOv7 as the baseline and propose an end-to-end feature fusion and feature enhancement YOLO (FE-YOLO). First, we introduce channel attention and lightweight Ghostconv into the extended efficient layer aggregation network of YOLOv7, resulting in the improved extended efficient layer aggregation network (IELAN) module. This improvement enables the model to capture context information better and thus enhance the target features. Second, to enhance the network's feature fusion capability, we design the light spatial pyramid pooling combined with the spatial channel pooling (LSPPCSPC) module and the coordinate attention feature pyramid network (CA-FPN). Furthermore, we develop an N-Loss based on normalized Wasserstein distance (NWD), effectively addressing the class imbalance issue in the ship dataset. Experimental results on the open-source Singapore maritime dataset (SMD) and SeaShips dataset demonstrate that compared to the baseline YOLOv7, FE-YOLO achieves an increase of 4.6% and 3.3% in detection accuracy, respectively.