Systems Engineering and Electronics ›› 2020, Vol. 42 ›› Issue (5): 1026-1034.doi: 10.3969/j.issn.1001-506X.2020.05.08

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Improved SSD algorithm for small-sized SAR ship detection

Juan SU1(), Long YANG1,2(), Hua HUANG3(), Guodong JIN1()   

  1. 1. College of Nuclear Engineering, Rocket Force University of Engineering, Xi'an 710025, China
    2. Unit 96873 of the PLA, Baoji 721016, China
    3. School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China
  • Received:2019-07-09 Online:2020-04-30 Published:2020-04-30
  • Supported by:
    国家自然科学基金(41574008)

Abstract:

Aiming at the disadvantages of low detection rate and high false alarm in small-sized ship detection for synthetic aperture radar (SAR) images, this paper proposes a small-sized SAR ship detection algorithm based on convolutional neural network. Firstly, a data set specially designed for small-sized SAR ship detection is constructed. Secondly, based on the single shot multibox detector (SSD) detection algorithm, improvements in transfer learning, bottom feature enhancement, and data augmentation are proposed. Using ResNet50 with better performance as the feature extraction structure, according to the basic principle of the inception module and the dilated convolution to expand the visual receptive field in the bottom feature enhancement, the algorithm enhances the adaptability of the network to small-sized ship targets. In the dataset of this paper, several sets of comparative analysis experiments are carried out. The experimental results show that the proposed method improves the average accuracy by 5.4% compared with the original SSD, and the missing detection and false alarms of small-sized SAR ships are obviously decreased.

Key words: object detection, convolutional neural network(CNN), transfer learning, bottom feature enhancement, dilated convolution

CLC Number: 

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