Ship Target Detection in SAR Image Based on Selective Coordinate Attention

被引：0
|
作者：
Yan C.-M. [1 ,2 ]
Wang C. [1 ]
机构：
[1] College of Physics and Electronic Engineering, Northwest Normal University, Gansu, Lanzhou
[2] Engineering Research Center of Gansu Province for Intelligent Information Technology and Application, Gansu, Lanzhou
来源：
Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2023年 / 51卷 / 09期
基金：
中国国家自然科学基金;
关键词：
convolutional neural network; feature extraction; selective coordinate attention; ship target detection; synthetic aperture radar;
D O I：
10.12263/DZXB.20211416
中图分类号：
学科分类号：
摘要：
Aiming at the high false alarm rate and missed detection rate of ship target detection results in SAR (Synthetic Aperture Radar) images, a ship target detection algorithm based on selective coordinate attention mechanism is proposed in this paper. The algorithm is based on a new selective coordinate attention mechanism. Firstly, the feature of ship target is extracted by the feature extraction branches of different convolution kernels. Then, the features of all branches are fused, and in order to capture the position information of the features in the spatial direction, the features are encoded along different spatial directions of the fused features to form two one-dimensional feature vector codes. Finally, this direction and position sensitive feature vector coding is used to form a“gate”mechanism to get the weighted fusion of the features extracted from receptive fields with different sizes of each branch, so as to enhance the feature representation of ship targets. In this paper, the SSD (Single Shot MultiBox Detector) algorithm is used as a benchmark to test the detection results of ship targets on the SSDD (SAR Ship Detection Dataset) data set. The experimental results show that, compared with other attention mechanisms, the selective coordinate attention mechanism improves the ship detection ability of the network model more effectively. At the same time, the average detection accuracy of the SSD algorithm based on selective coordinate attention mechanism is improved to 94.20%, which is 4.45% higher than the original SSD algorithm. In addition, further tests on the other two ship data sets show that the improved algorithm has good generalization and its comprehensive performance is better than the comparison algorithms. © 2023 Chinese Institute of Electronics. All rights reserved.
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收藏
页码：2481 / 2491
页数：10
相关论文
共 27 条
  • [1] PENG S J, QU C W, LI J W., K nearest neighbors optimized estimation algorithm for SAR image statistical modeling and target detection, Control and Decision, 35, 9, pp. 2199-2206, (2020)
  • [2] WACKERMAN C C, FRIEDMAN K S, PICHEL W G, Et al., Automatic detection of ships in RADARSAT-1 SAR imagery, Canadian Journal of Remote Sensing, 27, 5, pp. 568-577, (2001)
  • [3] TANG M E, LIN T Q, WEN G J., Overview of ship detection methods in remote sensing image, Application Research of Computers, 28, 1, pp. 29-36, (2011)
  • [4] QIN X X, ZHOU S L, ZOU H X, Et al., A CFAR detection algorithm for generalized gamma distributed background in high-resolution SAR images, IEEE Geoscience and Remote Sensing Letters, 10, 4, pp. 806-810, (2013)
  • [5] CHENG X, SONG C, SHI J G, Et al., A survey of generic object detection methods based on deep learning, Acta Electronica Sinica, 49, 7, pp. 1428-1438, (2021)
  • [6] GIRSHICK R., Fast R-CNN, 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1440-1448, (2016)
  • [7] REN S Q, HE K M, GIRSHICK R, Et al., Faster R-CNN: Towards real-time object detection with region proposal networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 6, pp. 1137-1149, (2017)
  • [8] REDMON J, DIVVALA S, GIRSHICK R, Et al., You only look once: Unified, real-time object detection, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779-788, (2016)
  • [9] REDMON J, FARHADI A., YOLO9000: Better, faster, stronger, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6517-6525, (2017)
  • [10] REDMON J, FARHADI A., YOLOv3: An incremental improvement
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