A Lightweight Radar Ship Detection Framework with Hybrid Attentions

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 11; p. 2743
• Main Authors: Yu, Nanjing, Ren, Haohao, Deng, Tianmin, Fan, Xiaobiao
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 25.05.2023
• Subjects: Algorithms; Artificial satellites in remote sensing; Complexity; convolutional neural network (CNN); Datasets; Deep learning; Design; Detectors; hybrid attention mechanism; Lightweight; Methods; Modules; Object recognition; Radar; Radar detection; Remote sensing; Sensors; ship detection; Synthetic aperture radar; synthetic aperture radar (SAR)
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15112743

One of the current research areas in the synthetic aperture radar (SAR) processing fields is deep learning-based ship detection in SAR imagery. Recently, ship detection in SAR images has achieved continuous breakthroughs in detection precision. However, determining how to strike a better balance between the precision and complexity of the algorithm is very meaningful for real-time object detection in real SAR application scenarios, and has attracted extensive attention from scholars. In this paper, a lightweight object detection framework for radar ship detection named multiple hybrid attentions ship detector (MHASD) with multiple hybrid attention mechanisms is proposed. It aims to reduce the complexity without loss of detection precision. First, considering that the ship features in SAR images are not inconspicuous compared with other images, a hybrid attention residual module (HARM) is developed in the deep-level layer to obtain features rapidly and effectively via the local channel attention and the parallel self-attentions. Meanwhile, it is also capable of ensuring high detection precision of the model. Second, an attention-based feature fusion scheme (AFFS) is proposed in the model neck to further heighten the features of the object. Meanwhile, AFFS constructs and develops a fresh hybrid attention feature fusion module (HAFFM) upon the local channel and spatial attentions to guarantee the applicability of the detection model. The Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0) experimental results demonstrate that MHASD can balance detection speed and precision (improving average precision by 1.2% and achieving 13.7 GFLOPS). More importantly, extensive experiments on the SAR Ship Detection Dataset (SSDD) demonstrate that the proposed method is less affected by the background such as ports and rocks.