Abstract: In recent years, there has been significant improvement in ship detection performance due to continuous innovation in ship target detection algorithms. However, deploying these algorithms in resource-limited environments poses certain challenges because of their high complexity and large scale. To address these issues, this paper proposes a new lightweight model GFPN-YOLOX. Firstly, the introduction of receptor field attention convolution into the backbone extraction network enhances the feature processing capability by extracting feature information from multi-scale targets. Secondly, a new feature pyramid fusion module is designed to improve information exchange between semantic information of remote sensing data and low-dimensional location information. Finally, deep separable convolution is introduced to reduce the complexity and storage requirements of the model. Compared to the original YOLOX-Tiny algorithm, this model reduces parameter count by 29% and calculation amount by 38%, while only losing accuracy by 0.4%. Its effectiveness in remote sensing data detection scenarios is verified compared to the latest algorithm.