提高合成训练声纳图像描述符在水下场所识别中的泛化能力
Improving Generalization of Synthetically Trained Sonar Image Descriptors for Underwater Place Recognition
AI总结：
本文提出一种仅用合成数据训练的紧凑型深度声纳描述符流水线，基于ResNet18和随机高斯投影层，结合归一化预滤波与定制合成数据生成，在水下场所识别中优于现有方法。
摘要：
水下环境中的自主导航面临光吸收和水体浑浊等因素带来的挑战，限制了光学传感器的有效性。声纳系统通常用于水下作业中的感知，因为它们不受这些限制的影响。传统计算机视觉算法在应用于声纳生成的声学图像时效果较差，而卷积神经网络（CNNs）通常需要大量标记训练数据，这些数据往往难以获得或难以获取。为此，我们提出了一种新颖的紧凑型深度声纳描述符流水线，该流水线仅在合成数据上训练即可泛化到真实场景。我们的架构基于ResNet18后端和一个适当参数化的随机高斯投影层，同时输入声纳数据通过标准的专用归一化/预滤波技术进行增强。还提出了一种定制的合成数据生成流程。所提出的方法已使用合成数据和公开可用的真实数据进行了广泛评估，证明了其与最先进方法相比的有效性。
英文摘要：
Autonomous navigation in underwater environments presents challenges due to factors such as light absorption and water turbidity, limiting the effectiveness of optical sensors. Sonar systems are commonly used for perception in underwater operations as they are unaffected by these limitations. Traditional computer vision algorithms are less effective when applied to sonar-generated acoustic images, while convolutional neural networks (CNNs) typically require large amounts of labeled training data that are often unavailable or difficult to acquire. To this end, we propose a novel compact deep sonar descriptor pipeline that can generalize to real scenarios while being trained exclusively on synthetic data. Our architecture is based on a ResNet18 back-end and a properly parameterized random Gaussian projection layer, whereas input sonar data is enhanced with standard ad-hoc normalization/prefiltering techniques. A customized synthetic data generation procedure is also presented. The proposed method has been evaluated extensively using both synthetic and publicly available real data, demonstrating its effectiveness compared to state-of-the-art methods.
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