针对常用的目标检测算法对遥感图像中的舰船目标进行检测时存在检测精度与实时性兼顾不佳的问题，提出了基于特征融合的遥感图像舰船目标检测算法来检测复杂场景下的多尺度舰船目标.该算法以多尺度单发射击检测框架为基础，增加反卷积特征融合模块和池化特征融合模块，增强网络特征提取的能力.同时设计聚焦分类损失函数来解决训练过程中正负样本失衡的问题.在高分遥感舰船目标数据集上的实验结果表明，所提方法能够有效地增强复杂场景下舰船目标的检测精度.此外，该算法对遥感图像中的模糊舰船目标的检测效果也优于多尺度单发射击检测框架.

Aiming at the problem of low accuracy and poor real-time performance of commonly used target detection algorithms, a novel ship target detection algorithm based on convolutional neural network with feature fusions is proposed to detect multi-scale ship targets in complex scenes. The proposed method inherits the network structure of SSD and introduces the deconvolution feature fusion module and the pooling feature fusion module into it to generate the new feature maps with richer semantic information for both ship classification and boxes regression. In addition, we used a focal classification loss function in the training strategy to deal with the imbalanced difficult and easy samples in the training process. The experiments tested on the ship detection dataset demonstrate that the proposed method shows a better adaptability to ship detection of different sizes in complex scenes. On the extended experiment, the proposed method performance over SSD in blurry object detection.