Weakly Supervised SAR Ship Oriented-Detection Algorithm Based on Pseudo-Label Generation Optimization and Guidance

Highlights: What are the main findings? We propose a weakly supervised method for oriented SAR ship detection that generates optimized pseudo-labels to guide the training process. The method uses only HBB labels to train an OBB detector, achieving performance and efficiency comparable to fully supervised methods. What are the implications of the main findings? The method effectively solves the problem of scarce OBB annotations, providing a practical solution for SAR ship oriented detection. The algorithm balances performance and efficiency, making it suitable for real-world applications on resource-limited platforms. In recent years, data-driven deep learning has yielded fruitful results in synthetic aperture radar (SAR) ship detection; weakly supervised learning methods based on horizontal bounding boxes (HBBs) train oriented bounding box (OBB) detectors using HBB labels, effectively addressing scarce OBB annotation data and advancing SAR ship OBB detection. However, current methods for oriented SAR ship detection still suffer from issues such as insufficient quantity and quality of pseudo-labels, low inference efficiency, large model parameters, and limited global information capture, making it difficult to balance detection performance and efficiency. To tackle these, we propose the weakly supervised oriented SAR ship detection algorithm based on optimized pseudo-label generation and guidance. The method introduces pseudo-labels into a single-stage detector via a two-stage training process: the first stage coarsely learns target angles and scales using horizontal bounding box weak supervision and angle self-supervision, while the second stage refines angle and scale learning guided by pseudo-labels, improving performance and reducing missed detections. To generate high-quality pseudo-labels in large quantities, we propose three optimization strategies: Adaptive Kernel Growth Pseudo-Label Generation Strategy (AKG-PLGS), Pseudo-Label Selection Strategy based on PCA angle estimation and horizontal bounding box constraints (PCA-HBB-PLSS), and Long-Edge Scanning Refinement Strategy (LES-RS). Additionally, we designed a backbone and neck network incorporating window attention and adaptive feature fusion, effectively enhancing global information capture and multiscale feature integration while reducing model parameters. Experiments on SSDD and HRSID show that our algorithm achieves an mAP50 of 85.389% and 82.508%, respectively, with significantly reduced model parameters and computational consumption.