Sar Ship Detection based on Swin Transformer and Feature Enhancement Feature Pyramid Network
With the booming of Convolutional Neural Networks (CNNs), CNNs such as VGG-16 and ResNet-50 widely serve as backbone in SAR ship detection. However, CNN based backbone is hard to model long-range dependencies, and causes the lack of enough high-quality semantic information in feature maps of shallow layers, which leads to poor detection performance in complicated background and small-sized ships cases. To address these problems, we propose a SAR ship detection method based on Swin Transformer and Feature Enhancement Feature Pyramid Network (FEFPN). Swin Transformer serves as backbone to model long-range dependencies and generates hierarchical features maps. FEFPN is proposed to further improve the quality of feature maps by gradually enhancing the semantic information of feature maps at all levels, especially feature maps in shallow layers. Experiments conducted on SAR ship detection dataset (SSDD) reveal the advantage of our proposed methods.
Code (0)
등록된 구현이 없습니다.
Tasks
SAR Ship DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
SAM-Swin: SAM-Driven Dual-Swin Transformers with Adaptive Lesion Enhancement for Laryngo-Pharyngeal Tumor Detection
Laryngo-pharyngeal cancer (LPC) is a highly lethal malignancy in the head and neck region. Recent advancements in tumor detection, particularly through dual-branch network architectures, have significantly improved diagn…
DiagnosticLesion SegmentationSwinV2DNet: Pyramid and Self-Supervision Compounded Feature Learning for Remote Sensing Images Change Detection
Among the current mainstream change detection networks, transformer is deficient in the ability to capture accurate low-level details, while convolutional neural network (CNN) is wanting in the capacity to understand glo…
Change DetectionTransformer-Based Explainable Deep Learning for Breast Cancer Detection in Mammography: The MammoFormer Framework
Breast cancer detection through mammography interpretation remains difficult because of the minimal nature of abnormalities that experts need to identify alongside the variable interpretations between readers. The potent…
Breast Cancer DetectionCBAM-SwinT-BL: Small Rail Surface Defect Detection Method Based on Swin Transformer with Block Level CBAM Enhancement
Under high-intensity rail operations, rail tracks endure considerable stresses resulting in various defects such as corrugation and spellings. Failure to effectively detect defects and provide maintenance in time would c…
Defect DetectionResolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection Strategy
The Transformer-based method has demonstrated remarkable performance for image super-resolution in comparison to the method based on the convolutional neural networks (CNNs). However, using the self-attention mechanism l…
Image CroppingImage RestorationImage Super-Resolutionobject-detection+3