nnMNet：火星地形语义分割基准模型
nnMNet: Baseline for Martian Terrain Semantic Segmentation
- National Cheng Kung University（成功大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出基于nnWNet的nnMNet基准模型，整合线性注意力与轻量级卷积，引入SAFB模块，结合新基准数据集，在三个火星地形数据集上取得mIoU新高，为火星语义分割提供可靠可复现基准。
AI中文摘要：
语义分割是理解火星（太阳系中与地球最相似的行星）的关键任务，但由于火星表面高度非结构化且复杂，精确的像素级预测和细粒度标注极具挑战性。深度学习的最新进展已推出大量方法和数据集以应对这些挑战，不过该领域仍缺乏稳健、公开可用且可复现的基准模型，以及用于公平评估的统一基准。本研究提出了专为火星地形语义分割设计的新基准模型nnMNet，其基于nnWNet构建，整合线性注意力以更好捕捉全局上下文，并采用轻量级卷积降低计算开销；为弥合局部与全局表征的差距，引入空间感知融合块（SAFB）来增强并融合具有不同特性的特征。此外，本研究通过整理和标准化三个高质量数据集建立了新基准，用于全面评估。nnMNet在SynMars-TW、SynMars-Air和MarsScapes上分别取得了86.61%、83.25%和88.24%的新最高平均交并比（mIoU），本研究的代码、模型和数据集可在指定公开链接获取。
英文摘要：
Semantic segmentation is a crucial task for understanding Mars, the most Earth-like planet in our solar system. However, it is challenging because the Martian surface is highly unstructured and complex, making accurate pixel-level prediction and fine-grained annotation difficult. Recent advancements in deep learning have introduced numerous methods and datasets to address these challenges. Nevertheless, the field lacks a robust, publicly available, and reproducible baseline, as well as a unified benchmark to facilitate fair evaluations. In this work, we present nnMNet, a new baseline model designed for Martian terrain semantic segmentation. Building upon nnWNet, we integrate linear attention to better capture global context and employ lightweight convolutions to reduce computational overhead. To bridge the gap between local and global representations, we introduce the Spatially-Aware Fusion Block (SAFB), which augments and combines features with diverse characteristics. Furthermore, we establish a new benchmark by curating and standardizing three high-quality datasets for thorough evaluation. nnMNet achieves new state-of-the-art 86.61%, 83.25%, and 88.24% mIoU on SynMars-TW, SynMars-Air, and MarsScapes, respectively. Our code, models, and datasets are publicly available at https://github.com/dereklee0310/nnMNet.
