AFMSFFNet: An Anchor-Free-Based Feature Fusion Model for Ship Detection

Abstract

This paper aims to improve a small-scale object detection model to achieve detection accuracy matching or even surpassing that of complex models. Efforts are made in the module design phase to minimize parameter count as much as possible, thereby providing the potential for rapid detection of maritime targets. Here, this paper introduces an innovative Anchor-Free-based Multi-Scale Feature Fusion Network (AFMSFFNet), which improves the problems of missed detection and false positives, particularly in inshore or small target scenarios. Leveraging the YOLOX tiny as the foundational architecture, our proposed AFMSFFNet incorporates a novel Adaptive Bidirectional Fusion Pyramid Network (AB-FPN) for efficient multi-scale feature fusion, enhancing the saliency representation of targets and reducing interference from complex backgrounds. Simultaneously, the designed Multi-Scale Global Attention Detection Head (MGAHead) utilizes a larger receptive field to learn object features, generating high-quality reconstructed features for enhanced semantic information integration. Extensive experiments conducted on publicly available Synthetic Aperture Radar (SAR) image ship datasets demonstrate that AFMSFFNet outperforms the traditional baseline models in detection performance. The results indicate an improvement of 2.32% in detection accuracy compared to the YOLOX tiny model. Additionally, AFMSFFNet achieves a Frames Per Second (FPS) of 78.26 in SSDD, showcasing superior efficiency compared to the well-established performance networks, such as faster R-CNN and CenterNet, with efficiency improvement ranging from 4.7 to 6.7 times. This research provides a valuable solution for efficient ship detection in complex backgrounds, demonstrating the efficacy of AFMSFFNet through quantitative improvements in accuracy and efficiency compared to existing models.