PMF-YOLOv8: Enhanced Ship Detection Model in Remote Sensing Images

Abstract

Satellite remote sensing technology plays a pivotal role in ship monitoring at sea, with ship detection by artificial intelligence becoming the primary means. However, due to the intricate marine environment and the similarity between classes of remote sensing ships, the detection of remote sensing ships still faces significant challenges. Existing detection models tend to overlook the loss of fine-grained features of remote sensing ships during the deepening of the network. To address this issue, we proposed an enhanced Pyramid for Multi-Scale Feature Fusion (PMF) to optimize the YOLOv8 algorithm. After incorporating a fusion of shallow-level features into the neck portion of YOLOv8, an adaptive spatial feature fusion approach coupled with a path aggregation network was employed to process the output features of the backbone network. This integration enhances the learning of fine-grained features and addresses the issue of feature loss, a common challenge in existing networks. Furthermore, to enhance feature extraction, we introduced an enhanced R-C2f module. Finally, Inner-MPDIoU was employed as the bounding box loss to address the issue of missed detections that may arise in the context of dense remote sensing ships. Experiments were conducted on FGSC-T, a dataset comprising 22 classes of ships, to assess the efficacy and viability of the algorithm. In comparison to the original YOLOv8, the mAP50, mAP50-95, Recall, and Precision increased by 3.7%, 4.1%, 5.7%, and 2.5%, respectively. Furthermore, the detection speed of PMF-YOLOv8 can reach 74 fps, which meets the requirements for real-time detection of remote sensing ships.