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Received：20 February 2021，
Published：07 September 2022
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针对当前遥感图像舰船目标检测精度不佳问题，本文构建舰船目标数据集STAR，提出基于Dense RFB和LSTM多尺度舰船目标检测算法。该算法首先在SSD网络基础上设计了浅层特征增强模块，基于人眼视点图采用Dense RFB特征复用和膨胀卷积增大感受野的尺度和种类，增强浅层网络对细节特征的提取能力；其次设计了深层多尺度特征金字塔融合模块，采用FPN和LSTM思想，基于反卷积和残差网络对深层不同尺度特征进行融合，增强网络结构非线性和特征层的表征能力；最后加入聚焦分类损失函数进行联合训练，有效避免了正负样本失衡问题。在遥感图像舰船数据集上实验，本文所提舰船目标检测算法精度均值达到
<math id="M1"><mn mathvariant="normal">81.98</mn><mi mathvariant="normal">%</mi></math>
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9.82133293
2.37066650
，检测速度达到29.6帧/s。此外，遥感图像中成像模糊、被遮挡、部分被裁剪等舰船目标的检测效果也优于原有经典算法，实验结果表明该算法对遥感图像舰船目标检测的泛化能力较强，有效地提高了遥感图像舰船目标检测的精度。
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.
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