Recently, deep learning has greatly promoted the development of SAR ship detection. But the detectors are usually heavy and computation intensive which hinder the usage on the edge. In order to solve this problem, a lot of lightweight networks and acceleration ideas are proposed. In this survey, we review the papers that about real-time SAR ship detection. We firstly introduce the model compression and acceleration methods. They are pruning, quantization, knowledge distillation, low-rank factorization, lightweight networks and model deployment. They are the source of innovation in real-time SAR ship detection. Then we summarize the real-time object detection methods. They are two-stage, single-stage, anchor free, trained from scratch, model compression and acceleration. Researchers in SAR ship detection usually learn from these ideas. We then spend a lot of content on the review of the 70 real-time SAR ship detection papers. The years, datasets, journals, deep learning frameworks, and hardwares are introduced firstly. After that, the 10 public datasets and the evaluation metrics are shown. Then, we survey the 70 papers according to anchor free, trained from scratch, YOLO series, CFAR+CNN, lightweight backbone, pruning, quantization, knowledge distillation and hardware deployment. The experimental results show that the algorithms have been greatly developed in speed and accuracy. In the end we pointed out the problems of the 70 papers and the directions to be studied in the future. Our work can enable researchers to quickly understand the research status in this field.
Due to the advantages of Synthetic Aperture Radar (SAR), the study of Automatic Target Recognition (ATR) has become a hot topic. Deep learning, especially in the case of a Convolutional Neural Network (CNN), works in an end-to-end way and has powerful feature-extracting abilities. Thus, researchers in SAR ATR also seek solutions from deep learning. We review the related algorithms with regard to SAR ATR in this paper. We firstly introduce the commonly used datasets and the evaluation metrics. Then, we introduce the algorithms before deep learning. They are template-matching-, machine-learning- and model-based methods. After that, we introduce mainly the SAR ATR methods in the deep-learning era (after 2017); those methods are the core of the paper. The non-CNNs and CNNs, that is, those used in SAR ATR, are summarized at the beginning. We found that researchers tend to design specialized CNN for SAR ATR. Then, the methods to solve the problem raised by limited samples are reviewed. They are data augmentation, Generative Adversarial Networks (GAN), electromagnetic simulation, transfer learning, few-shot learning, semi-supervised learning, metric leaning and domain knowledge. After that, the imbalance problem, real-time recognition, polarimetric SAR, complex data and adversarial attack are also reviewed. The principles and problems of them are also introduced. Finally, the future directions are conducted. In this part, we point out that the dataset, CNN architecture designing, knowledge-driven, real-time recognition, explainable and adversarial attack should be considered in the future. This paper gives readers a quick overview of the current state of the field.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.