A Survey on Deep-Learning-Based Real-Time SAR Ship Detection

Abstract

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.

YOLO-OSD: Optimized Ship Detection and Localization in Multiresolution SAR Satellite Images Using a Hybrid Data-Model Centric Approach

“…Similarly, Cui et al [36] proposed a model based on Dense Attention Pyramid Network and also compared its performance with Faster RCNN and SSD. Comprehensive surveys have also been conducted on SAR ship detection using deep learning techniques [37], [38].…”

“…Figure 8 shows the outputs of K-Means clustering with K = 12. (11,12), (24,13), (13,29), (16,44), (44,18), (37,35), (23,59),(107,31), (40,102),(79,53), (54,169),(199,67)…”

“…SAR-Ships YOLO-OSD (Proposed) 12 (12,14), (22,16), (13,30), (37,17), (24,28), (20,44), (39,27), (37,41), (53,35), (27,73), (42,60),(74,68)…”

An Effective Multi-Layer Attention Network for SAR Ship Detection

“…Other hyperparameters are the same as the default values in MMDetection. Four assessment indicators, precision, recall, F1 score, and AP (short for "average precision", a quality assessment index used in the literature), as given below [41], were used to gauge how well our proposed strategy performed.…”

DBW-YOLO: A High-Precision SAR Ship Detection Method for Complex Environments

“…S YNTHETIC Aperture Radar (SAR) stands as an active microwave remote sensing device employing virtual array and pulse compression technologies to acquire high-resolution two-dimensional images of ground objects. Because of its unique system and rich polarization information, SAR images find extensive use in various civilian and military applications [1], [2], [3] such as urban planning, environmental and natural disaster monitoring, and ship target detection. Among them, ship target detection serves the purpose of identifying and locating vessels automatically and holds significant importance in monitoring and ensuring the security of coastal areas and waterways.…”