A Lightweight Faster R-CNN for Ship Detection in SAR Images

Deep learning algorithms have been widely utilized for synthetic aperture radar (SAR) target detection. Nevertheless, the traditional feature extraction methods and deep learning methods achieve improved ship detection accuracy at a cost of increased complexity and lower detection speed. As detection speed also is meaningful, especially in real-time maritime rescue and emergency military decision-making applications, we propose a new framework of faster region-based convolutional neural network (R-CNN) detection method to handle this problem. A new lightweight basic network with feature relay amplification and multiscale feature jump connection structure is designed to extract the features of each scale target in the SAR images, so as to improve its recognition and localization task network. Moreover, the K-Means method is used to obtain the distribution of the target scale, which enables to select more appropriate preset anchor boxes to reduce the difficulty of network learning. Finally, RoIAlign instead of region of interest (RoI) Pooling is used to reduce the quantization error during positioning. Experimental results show that the detection performance of the proposed method achieves 0.898 average precision (AP), which is 2.78% better than the conventional Faster R-CNN and 800% faster detection speed.

Automated summary

A new and faster region-based convolutional neural network (R-CNN) detection method is proposed in this paper, with a new lightweight network design and the use of K-Means method to optimize the recognition of target scale. The proposed method shows significant improvements in both detection performance and speed.