基于局部显著特征聚焦学习的SAR舰船智能检测

Local Salient Feature-focused Learning for SAR Ship Detection

合成孔径雷达(Synthetic Aperture Radar,SAR)图像的船舶目标检测,因其广泛的应用前景而备受关注。近年来,基于深度学习的SAR图像船舶目标检测在多种场景中表现出较好性能。然而,由于SAR独特的成像机制,舰船目标通常与背景环境具有相似的散射特性使得实际的船舶目标难以辨识,且船舶目标尺度较小,导致准确检测船舶目标具有挑战性。为了缓解这一问题,本文提出了一种基于局部显著特征聚焦学习的SAR舰船检测方法。首先,设计了双重注意力模块,通过对通道级和空间级的特征进行双重注意力加权,以充分地探索船舰目标的关键语义特征,从而提升模型的深度提取能力。随后,为了进一步提升模型对船舶目标特征的表征能力,设计了平衡特征金字塔网络模块,通过对舰船目标的多尺度特征进行缩放、增强和聚合处理,以实现多尺度特征间的语义和空间信息均衡分布。最后,在SAR舰船检测数据集(SAR Ship Detection Dataset,SSDD)上进行了广泛的实验分析,实验结果一致性地证明了所提方法在提升SAR图像舰船目标检测准确性方面的有效性。

Ship target detection in Synthetic Aperture Radar(SAR)images has gained substantial attention due to its broad application prospects in the military and civilian domains.With the recent rapid advancements in deep learning technologies,SAR ship target detection based on deep learning methods has demonstrated promising performance across various scenarios.It is thus becoming a research hotspot nationally and internationally.However,due to the unique imaging characteristic of SAR,ship targets usually have similar scattering characteristics with the background environment,making it difficult to identify actual ship targets,and the ships’scales are relatively small,making it challenging to detect ship targets accurately.In this study,we propose a novel local salient feature-focused learning approach for SAR ship detection,which can effectively improve the accuracy of ship target detection and address these issues.First,a dual-attention module(DAM)is designed to provide a dual-attention weighting mechanism for features on both channel and spatial levels.This facilitates thoroughly exploring the critical semantic information associated with ship targets,thereby increasing the model’s ability to understand the nuances between the ship’s target and the background environment.Subsequently,to further enhance the model’s representation capability of ship target features,a balanced feature pyramid network(BFPN)module is introduced.This module achieves a balanced distribution of semantic and spatial information among different feature scales,by scaling enhancing,and aggregating multi-scale features of ship targets.As a result,the model can capture the ship target features contained in different feature scales more comprehensively,and the semantic clues of ship targets are effectively integrated,thus improving ship detection accuracy.Extensive experimental analyses are conducted on the SAR Ship Detection Dataset(SSDD)to validate the method’s effectiveness.For the evaluation metrics,Precision,Recall,F1-score,and mean Average Precision(mAP)are selected as the evaluation metrics for algorithm performance.In addition,visualization of the detection results of different methods under different scenarios is provided.The quantitative and qualitative results consistently show the effectiveness of the model in improving the detection accuracy of ship targets.This study contributes to the development of SAR ship detection methods and improves their practicality and realistic usability.In conclusion,this study presents a SAR ship detection method that leverages local salient feature-focused learning.The method demonstrates enhanced performance in detecting ship targets in SAR imagery through dual-attention and balanced feature pyramid network modules.This holds significant implications for military and civilian applications and contributes to the broader field of deep learning and computer vision.It is worth noting that the proposed method only focuses on enhancing and refining the features of ship targets at each layer.While the topological features of ship targets are important physical structure information of SAR images,using them to enhance the performance of ship target detection will be the focus of future studies.