针对内河船舶航行场景复杂、航海雷达图像形状与颜色特征较少且难以标注的问题，提出一种改进的YOLOv8航海雷达图像目标检测方法。首先，为缓解标注错误与模型过拟合问题，在模型训练阶段引入标签平滑策略；然后，结合雷达图像特有的位置先验信息，设计一种基于坐标的卷积结构用于同时提取目标的形状、颜色和位置特征。为验证该方法的有效性和优越性，对采集的长江航道雷达图像在不同天气环境下进行对比试验。结果表明，本文方法在保证目标检测实时性的同时，精确率达到91.52%，平均精度较经典YOLOv8提高了5.17%，可为提升内河航运现代化与智能化管理水平提供技术支持。

In order to solve the problems of complex navigation scenes of inland waterway vessels, few shape and color characteristics of marine radar images, and the difficulty of annotation, an improved YOLOv8 marine radar image target detection method was proposed. Firstly,  to alleviate the issues of annotation errors and model overfitting, a label smoothing strategy was introduced during the model training phase. Then, combining the unique positional prior information of the radar images, a coordinate-based convolution structure was designed to simultaneously extract the shape, color and positional features of the target. To verify the effectiveness and superiority of the proposed method, comparative experiments were conducted on the collected radar images of the Yangtze River channel  under different weather conditions. Results show that the proposed method achieves an accuracy rate of 91.52% while ensuring real-time object detection, with an average accuracy  improvement of  5.17% compared to the classic YOLOv8, which can provide technical support for improving the modernization and intelligent management level of inland waterway shipping.