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Ship detection in remote sensing image based on dense RFB and LSTM

  • role： First author第一作者
  • Affiliation：

    College of Missile Engineering, Rocket Force University of Engineer, Xi'an 710025, China

  • Email：sunshinetaoz@163.com
  • Introduction：张涛，研究方向为遥感图像处理、目标检测等理论和应用。E-mail：sunshinetaoz@163.com
ZHANG Tao1,  
  • Affiliation：

    College of Missile Engineering, Rocket Force University of Engineer, Xi'an 710025, China

YANG Xiaogang1,  
  • Affiliation：

    Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710068, China

LU Xiaoqiang2,  
  • Affiliation：

    College of Missile Engineering, Rocket Force University of Engineer, Xi'an 710025, China

LU Ruitao1,  
  • Affiliation：

    College of Missile Engineering, Rocket Force University of Engineer, Xi'an 710025, China

ZHANG Shengxiu1

Resümee

Ship detection plays a crucial role in various applications and has drawn increasing attention in recent years. Deep learning methods based on CNNs, particularly SSD, have greatly improved detection performance due to their highly efficient feature extraction capability. However, SSD still has two problems. For instance, the detection network of arbitrarily arranged ship targets lacks a connection between high and low-level features and ignores contextual semantic information. Another problem is that natural factors such as light and clouds affect remote sensing images, thus ship detection may cause an imbalance of positive and negative samples.Aiming at solving the above issues, this paper proposes to achieve ship detection in remote sensing images by using a method based on Dense RFB and LSTM. This proposed method includes three elements. First, to enhance the detail feature extraction capability, this proposed method introduces a shallow feature enhancement module. This module draws on the idea of the human viewpoint, which uses Dense RFB feature reuse and expansion convolution to increase the receptive field. Second, to effectively extract deep semantic information and enhance the expressive ability of the network feature layer, a deep multi-scale feature pyramid fusion module (MFPF) is designed, as this proposed method draws on FPN and LSTM deconvolution and residual structure fuse deep multi-scale features. Finally, to solve the imbalance of positive and negative samples, the focal classification loss function is added, improving the accuracy of ship detection during training.The experiments were carried out on an optical remote sensing image dataset, in which only the ship dataset was used for training, validation, and testing. Results indicate that the proposed algorithm achieved an Average Precision (AP) of 81.98% and the detection speed reached 29.6 fps for ship targets, in which most ships were detected successfully. Moreover, for blurred, occluded, and partially-cropped ship targets, the algorithm’s detection effect is better than the traditional algorithm. Qualitative and quantitative results indicate that the generalization capability of the proposed method enhances ship detection.From this paper, we can draw three conclusions: (1) The proposed method can improve the extraction of detailed features and increase the receptive fields. (2) The focal loss function method shows good generalization capability. (3) The rotating box detection method is suitable for multi-scale and densely-arranged remote sensing images.

Schlüsselwort

ship target detection;Dense RFB;feature pyramid networks;LSTM;multi-scale feature

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