用于侧扫声纳数据语义分割的卷积视觉Transformer
A Convolutional Vision Transformer for Semantic Segmentation of Side-Scan Sonar Data
AI总结：
针对侧扫声纳海底数据分类，提出基于ViT的编码器-解码器架构，引入新模块替换MLP并提取多尺度特征，结合轻量级解码器，在低数据量下实现最先进结果并满足实时计算要求。
摘要：
区分不同的海洋底栖栖息地特征在从安装石油钻井平台到铺设电缆网络以及监测人类对海洋生态系统影响等广泛的海底作业中至关重要。侧扫声纳（SSS）是一种广泛使用的成像传感器。它通过记录从海底反射回来的声波强度来生成高分辨率的海底地图。在本研究中，我们利用这些声学强度图来实现不同海底类型的逐像素分类。我们提出了一种基于视觉Transformer（ViT）改编的新型架构，采用编码器-解码器框架。此外，在这样做的同时，我们评估了ViT在较小数据集上的适用性。为了克服缺乏类似CNN的归纳偏置，从而使ViT更适合低数据量场景下的应用，我们提出了一种新型特征提取模块来替换Transformer层内的多层感知机（MLP）块，并提出了一种新型模块来提取多尺度补丁嵌入。同时提出了一种轻量级解码器来补充这一设计，以进一步提升多尺度特征提取能力。通过修改后的架构，我们实现了最先进的结果，并满足了实时计算要求。我们在 ~\url{https://github.com/hayatrajani/s3seg-vit} 上公开了代码。
英文摘要：
Distinguishing among different marine benthic habitat characteristics is of key importance in a wide set of seabed operations ranging from installations of oil rigs to laying networks of cables and monitoring the impact of humans on marine ecosystems. The Side-Scan Sonar (SSS) is a widely used imaging sensor in this regard. It produces high-resolution seafloor maps by logging the intensities of sound waves reflected back from the seafloor. In this work, we leverage these acoustic intensity maps to produce pixel-wise categorization of different seafloor types. We propose a novel architecture adapted from the Vision Transformer (ViT) in an encoder-decoder framework. Further, in doing so, the applicability of ViTs is evaluated on smaller datasets. To overcome the lack of CNN-like inductive biases, thereby making ViTs more conducive to applications in low data regimes, we propose a novel feature extraction module to replace the Multi-layer Perceptron (MLP) block within transformer layers and a novel module to extract multiscale patch embeddings. A lightweight decoder is also proposed to complement this design in order to further boost multiscale feature extraction. With the modified architecture, we achieve state-of-the-art results and also meet real-time computational requirements. We make our code available at ~\url{https://github.com/hayatrajani/s3seg-vit
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
