Lightweight network for fast ship detection in SAR images

doi:10.7523/j.ucas.2023.017

Abstract

Abstract: In the field of SAR image ship detection based on deep learning, traditional models are usually complex in structure and require a large amount of calculation, making them unsuitable for low computing power platforms and real-time detection. And convolutional neural networks that rely on preset anchor boxes will lead to a lot of computational redundancy due to the difficulty of setting a reasonable anchor box. To solve these problems, an end-to-end lightweight convolutional neural network based on anchor-free design is proposed, and a lightweight channel attention module (EESE) is designed and applied to the detection head (ED-head), to resolve the conflict between classification and localization tasks. In addition, an optimized EIOU loss function is proposed, which enables the model to effectively improve the network performance without increasing the inference time. The proposed method is tested on the SSDD dataset, and the experimental results show that compared to YOLOX-nano, AP₅₀ and AP are increased by 2.1 and 7.4 percentage points, respectively, with the CPU latency being only 5.33 ms, much less than 13.13 ms of YOLOX-nano. The proposed method achieves a balance between accuracy and efficiency.

Key words: synthetic aperture radar (SAR), ship detection, deep learning, lightweight network, anchor-free target detection

CLC Number:

TP39

ZHOU Wenxue, ZHANG Huachun. Lightweight network for fast ship detection in SAR images[J]. Journal of University of Chinese Academy of Sciences, 2024, 41(6): 776-785.

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