Style Transfer-Based Data Augmentation for Side-Scan Sonar Image Classification
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摘要: 侧扫声呐(SSS)因其在自主水下航行器(AUV)上的稳定性与高效性, 在海洋探测领域得到了广泛应用。然而, SSS图像获取难度大、样本数量稀少, 严重限制了基于深度神经网络(DNN)的SSS图像分类性能。针对这一问题, 文中提出了一种多尺度注意力融合网络(MSANet), 利用光学-声学图像对进行数据扩充来提升SSS图像分类模型的泛化能力。首先, 通过编码器不同层提取输入图像的浅层与深层特征, 以充分捕捉内容与风格信息。随后, 引入多尺度注意力模块(MSAM), 提取风格图像在通道维度上的局部与全局上下文信息, 并与光学特征进行高效融合, 使光学特征在不同空间位置精准匹配相应的声学特征。最终, 将不同层融合后的特征进行尺度对齐, 并输入到解码器生成高质量的模拟SSS图像样本, 用于训练SSS图像分类网络。在真实SSS图像数据集上的实验结果表明, 提出的风格迁移方法能够有效生成高质量模拟SSS图像样本, 进而提高基于DNN的SSS图像分类性能。Abstract: Side-scan sonar (SSS) has been widely employed in ocean exploration due to its stability and efficiency on autonomous underwater vehicles (AUVs). However, the difficulty in acquiring SSS images and the limited availability of training samples significantly limit the performance of the deep neural network (DNN)-based SSS image classification. To address this challenge, this paper proposes a multi-scale attention network (MSANet) that utilizes optical-acoustic image pairs for data augmentation to enhance the generalization ability of SSS image classification models. Specifically, shallow and deep features are extracted from different layers of the encoder to comprehensively capture content and style information. Subsequently, a multi-scale attention module (MSAM) is introduced to extract both local and global contextual information of style features along the channel dimension. These style features are then effectively fused with optical features to achieve precise spatial alignment of optical and acoustic features. Finally, the fused features from different layers are rescaled and fed into a decoder to generate high-fidelity synthetic SSS image samples, which are used to train the SSS image classification network. Experimental results on real-world SSS datasets demonstrate that the proposed style transfer approach effectively generates high-quality synthetic SSS image samples, thereby improving the DNN-based SSS image classification performance.
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Key words:
- side-scan sonar (SSS) /
- style transfer /
- self-attention mechanism /
- image classification
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表 1 使用不同方法进行图像扩充的分类实验结果
Table 1. Classification experimental results using different methods for image augmentation t
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表 2 不同方法合成图像训练分类器的分类实验结果
Table 2. Classification experimental results of classifiers trained with synthetic images using different methods
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表 3 不同分类器的对真实SSS图像的分类实验结果
Table 3. Classification experimental results of real SSS images using different classifiers
方法 图像张数 识别精度 飞机 沉船 Vgg-19 21 209 83.94% ResNet-152 23 218 87.96% ViT-B/16 23 213 86.13% EfficientNet-v2s 25 215 87.59%
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