Due to the complex background of ship targets and much irrelevant interference in visual images, it is difficult to conduct ship detection. In addition, there are few datasets for multi-category ship detection and the samples are often unbalanced, which makes the ship target detection performance degraded. Considering the ship detection background interference, an improved YOLOv3 model was proposed by introducing SimAM attention mechanism, which was used to enhance the weight of the ship target in the extracted features and suppress the weight of background interference, thus improving the model detection performance. Meanwhile, strong real-time data augmentation was applied to improve the unbalanced distribution of sample scales, and transfer learning was combined to improve the ship detection accuracy in the condition of a restricted number of samples. The visualization results of extracted features show that the improved model could suppress irrelevant background features, and the abilities of feature extraction and target localization were enhanced. Without introducing additional learnable parameters, the proposed model achieved 96.93% and 71.49% for mAP.5 and mAP.75 on the SeaShips dataset, and detection speed reached 66 frames per second, indicating a good balance between detection accuracy and efficiency. The improved model optimized the target features more effectively compared with the Saliency-aware CNN and eYOLOv3 models, resulting in an improvement of mAP.5 by 9.53% and 9.19%. The mAP.5 for ship type target detection on Singapore Maritime Dataset reached 81.81%, indicating that the proposed model has good generalization performance.