The existing ship object detection algorithms mostly rely on optimized improvements based on traditional object detection algorithms, without considering the scale and aspect ratio characteristics of ships, leading to issues such as missed detections and false detections in multi-scale object detection. To address this, the paper proposed a scale-adaptive receptive field ship detection method (SAF-YOLOX) based on YOLOXs. Firstly, it extracted different feature layers by the backbone network, which were fused by constructing a bidirectional feature pyramid, improving feature representation at various scales. Simultaneously, it designed an adaptive feature enhancement module to suppress redundant information introduced by the fusion of features at different scales, thereby attenuating background information. During the prediction phase, it employed a multi-branch parallel receptive field detection head, utilizing receptive fields adapted to target sizes and proportions for extracting scale-adaptive feature information. Additionally, it implemented a convergence-aware strategy, dynamically selecting and allocating samples based on the network s convergence state. This strategy ensured improved detection accuracy while maintaining detection speed. Experimental results demonstrate that the proposed method achieves an average detection accuracy of 93. 21% on the SeaShips dataset and 92.34% on the MCShips dataset. When compared to traditional YOLOXs, the method exhibits an improvement of 1.01% and 1.09%, respectively. The experimental results confirm that the proposed method, utilizing scale-adaptive receptive fields, can achieve high-precision detection of multi-scale ship targets. [ABSTRACT FROM AUTHOR]