A Study on Small-Scale Ship Detection Based on Attention Mechanism

文献类型： 外文期刊

第一作者： Zheng, Jianli

作者： Zheng, Jianli;Liu, Yang

作者机构：

关键词： Feature extraction; Marine vehicles; Deep learning; Object detection; Training; Optical sensors; Monitoring; Ship detection; attention mechanism; SSD; default box

期刊名称：IEEE ACCESS （ 影响因子：3.476； 五年影响因子：3.758 ）

ISSN： 2169-3536

年卷期： 2022 年 10 卷

页码：

收录情况： SCI

摘要： In ship detection based on optical images, the system typically needs to handle small-scale targets in complex environments owing to special application scenarios, and small-scale targets are easily ignored after a multi-layer convolution is applied in deep learning. A small-ship detection method based on an attention mechanism is therefore proposed in this study. The local attention module acts on the bottom feature prediction layer to highlight the key features and improve the detection ability of small target objects. Meanwhile, a high-level feature prediction layer is combined to classify, detect, and improve the recognition accuracy of the model. In this study, training was conducted on the SeaShips dataset. Because the SeaShips dataset is of a single type and consists of ships of roughly the same size, we changed the size of the default box, which not only improves the detection speed, it also significantly improves the detection accuracy compared with a conventional SSD algorithm.

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