An Improved Algorithm for Ship Detection in SAR Images Based on CNN

Aiming at the problem of low accuracy of ship detection in SAR images, we propose an improved detection method based on RetinaNet. This method introduces channel-wise attention mechanism into the backbone feature extraction network, in order to automatically obtains the importance of each feature channel by means of learning, and then enhances the useful features according to this importance and restrains the features that are not of much use to the detection task. In order to improve the capability of multi-scale detection, this method also introduces an efficient weighted bidirectional feature fusion network-BiFPN, which adjusts the proportion of each feature by learning the importance of features of different scales. In addition, we propose a training method to expand the complex background samples in the data set to improve the classification performance of the network to the targets and complex background. Training and testing with open SAR image ship detection datasets, the detection results show that this method can significantly improve the precision and recall rate.