HFPNet: Super Feature Aggregation Pyramid Network for Maritime Remote Sensing Small-Object Detection

In recent years, with the development of the maritime industry, computer vision tasks based on optical remote sensing images have gained increasing attention in the field of marine remote sensing. However, in complex marine meteorological environments, traditional detection methods often suffer from high rates of missed detections and false alarms for small targets. To address this issue, this article proposes a super feature pyramid network (HFPNet) for detecting marine remote sensing objects, which includes a feature enhancement module (FEM) and a multiscale feature aggregation module (MFM). The FEM can highlight features of small targets and weaken complex background features in detection, the MFM can share high-level and low-level features to better fuse multiscale feature information. Additionally, due to the lack of marine remote sensing object datasets, this article constructs a marine target detection dataset (MTDS) containing six types of marine objects. To address the issue of imbalanced positive and negative samples in the dataset, this article designs a network loss function to accelerate network convergence and improve the accuracy of small-target detection. Compared with other models, HFPNet achieved the highest
$\text{mAP}_{0.5}$
of 97.48% and 95.46% on the self-built dataset MTDS and the public dataset NWPU-VHR-10, respectively, after environmental enhancement. At the same time, it also achieved the fastest frames per second (FPS) on the test set. Finally, this article discusses the influence of attention mechanisms and postprocessing methods on HFPNet and obtains the best small-target detection model.