Ship’s critical part detection algorithm based on anchor-free in optical remote sensing

被引：0

作者：

Zhang D. ^{[1
]}

Wang C. ^{[1
]}

Fu Q. ^{[1
]}

机构：

[1] Department of Electronic and Optical Engineering, People Liberation Army Engineering University-Shijiazhuang, Shijiazhuang

来源：

Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2024年 / 50卷 / 04期

关键词：

anchor-free; critical part detection; deep learning; fully convolutional one-stage object detection; remote sensing; ship detection;

D O I：

10.13700/j.bh.1001-5965.2022.0450

中图分类号：

学科分类号：

摘要：

Low detection effectiveness and inadequate refinement plague the existing deep learning-based remote sensing ship detection technique. To address the above problems, an optical remote sensing ship critical part detection algorithm based on anchor-free is proposed. The proposed algorithm takes fully convolutional one-stage object detection (FCOS) as the benchmark algorithm and introduces a global context module in the backbone network to improve the feature representation capability of the network. In the prediction step, a regression branch with orientation representation capabilities is built to more accurately describe the orientation of targets. The centrality function is optimized to make it direction-aware and adaptive. The experimental results show that the average precision (AP) of the proposed algorithm is significant improved over FCOS algorithm on the self-built ship critical part dataset and HRSC2016, respectively. Compared with other algorithms, the proposed algorithm has superior performance in both detection speed and detection accuracy and has high detection efficiency. © 2024 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.

引用

页码：1365 / 1374

页数：9

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