针对内河环境下小型船舶目标检测精度较低，以及船舶目标检测易受近岸复杂背景信息影响的问题，基于YOLOv5s做出改进，提出SACAM-YOLOv5船舶目标检测算法。首先，引入上下文增强模块，利用空洞卷积特性，获取上下文信息，加强模型对小型船舶目标检测能力；其次，利用可切换空洞卷积特性，设计C3_SAC模块用于取代原主干网络中特定位置的C3模块，通过扩大主干网络提取特征图感受野，增强特征图所含信息，减少复杂背景影响，从而提升网络特征提取能力；最后，改进损失函数，引入NWD且与CIoU_Loss共同作为新的边界框损失计算函数，降低小目标检测中位置偏差影响，提升小型船舶目标检测性能。使用Seaships船舶数据集对上述改进方法进行验证，结果表明，本文SACAM-YOLOv5算法相较基准模型，在精度、召回率和mAP@0.5上分别提升2.2%，2.0%和2.7%，分别达到98.6%，98.1%和99.5%，满足船舶目标检测性能要求，具有一定的工程实际意义。

Aiming at the low target detection accuracy of small ships in inland river environment and the problem that ship target detection is easily affected by complex background information near shore, a ship target detection algorithm SACAM-YOLOv5 was proposed based on improved YOLOv5s. Firstly, a context augmentation module was introduced to obtain context information by using the dilated convolution property, and to enhance the detection capability of the model on small ships. Secondly,  C3_SAC module was designed to replace C3 modules at specific positions in the original backbone network by using switchable dilated convolutions' feature. By expanding the receptive field of the feature map extracted by the backbone network, the information contained in the feature map was enhanced, the influence of complex background was reduced, thereby the feature extraction capability of the network was improved. Finally, the loss function was improved, and NWD was introduced and together with CIoU_Loss as a new bounding box loss calculation function to reduce the influence of position deviation in small target detection, and improve the detection performance for small ships. Seaships dataset was used to verify the above proposed method. Results show that compared with the benchmark model, the precision, recall, and mAP@0.5 of  SACAM-YOLOv5 algorithm are improved by 2.2%, 1.0%, and 2.7%, reaching 98.6%, 98.1% and 99.5% respectively, which meet the requirements of ship target detection performance with certain practical engineering significance.