物联网学报2024，Vol.8Issue(4)：156-166,11.DOI:10.11959/j.issn.2096-3750.2024.00407
基于注意力机制的轻量级SAR船舶检测器
Lightweight attention-based SAR ship detector
摘要
Abstract
Synthetic aperture radar(SAR)remote sensing images have been widely applied in military reconnaissance and traffic supervision,owing to their all-weather and all-day abilities.With excellent learning performance,convolutional neural networks are employed in the SAR ship detection algorithms.However,it is difficult to extract features.In practical appli-cations,computing resources and memory space are limited,and high inference speed is required.Therefore,a light-weight attention-based ship detector(LASD)was proposed.A novel linear hybrid attention module was designed which extracted potential ship features from deep-level space by using global channel attention and local spatial attention.A spatial pyramid pooling module based on cross-stage partial connections optimized the quality of multi-scale feature fu-sion,which replaced the parallel max-pooling group with large kernels with the serial max-poolings with small kernels to improve the inference speed.A novel feature fusion scheme via the local channel attention was suggested which widened the gap between the objects and background noise using local attention during the feature fusion.The results on the public datasets SSDD and LS-SSDD-v1.0 show that LASD achieves the balance of detection precision and inference speed,and is more competitive than the other advanced algorithms.关键词
SAR/船舶检测/卷积神经网络/注意力机制/多尺度特征融合Key words
SAR/ship detection/convolutional neural network/attention mechanism/multi-scale feature fusion分类
信息技术与安全科学引用本文复制引用
于楠晶,冯大权,朱颖,张恒嘉,陆平..基于注意力机制的轻量级SAR船舶检测器[J].物联网学报,2024,8(4):156-166,11.基金项目
深圳市自然科学基金资助项目(No.JCYJ20210324095209025)The Shenzhen Natural Science Foundation(No.JCYJ20210324095209025) （No.JCYJ20210324095209025）
