Lightweight SAR Ship Detection via Pearson Correlation and Nonlocal Distillation

Abstract

Aiming to the challenge of efficient synthetic aperture radar (SAR) ship detection, knowledge distillation recently gained increasing attention as an effective model lightweight approach. SAR ship detection faces challenges including small target detection and complex background clutter. Most existing knowledge distillation methods impose overly strict constraints on the student model, leading to insufficient extraction of detailed features for small target detection. In addition, current mainstream convolutional neural networks (CNNs) primarily focus on extracting local features, which are often inadequate for effectively distinguishing targets from background in complex environments. To address these issues, this proposed work proposes the Pearson correlation distillation and nonlocal distillation (PND) algorithm for SAR ship detection. The Pearson correlation coefficient (PCC) is utilized to model features, relaxing the constraints on the magnitude of the student model’s features. The nonlocal module captures long-range dependencies, enhancing adaptability to complex backgrounds. Experimental results on the SSDD and AIR-SARShip-1.0 datasets demonstrate that our method effectively improves the detection performance of the student model, while also facilitating its transfer to other detectors.