基于改进型YOLO算法的遥感图像舰船检测

目标检测算法在PASCAL VOC等数据集中取得了非常好的检测效果，但是在大尺度遥感图像中舰船目标的检测准确率却很低。因此，针对可见光遥感图像的特点，在YOLOv3-Tiny算法的基础上增加了特征映射模块，为预测层提供丰富的语义信息，同时在特征提取网络中引用残差网络，提高了检测准确率，从而有效提取舰船特征。实验结果表明:优化后的M-YOLO算法检测准确率为94.12%。相比于SSD和YOLOv3算法，M-YOLO算法的检测准确率分别提高了11.11%和9.44%。 

Remote sensing image ship detection based on modified YOLO algorithm

Although the target detection algorithm has achieved very good detection results in data sets such as PASCAL VOC.However, the accuracy of ship target detection in large-scale prediction images is very low.Therefore, according to the characteristics of the visible light reflection image, a feature mapping module is added on the basis of the YOLOv3-Tiny algorithm, which provides rich semantic information for the prediction layer.At the same time, a residual network is used in the feature extraction network, which improves the detection accuracy and effectively extracts ship features. Experimental results show that the detection accuracy of the optimized M-YOLO algorithm is 94.12%.Compared with the SSD and YOLOv3 algorithms, the detection accuracy of the M-YOLO algorithm is improved by 11.11% and 9.44%.