LisseMars: A Lightweight Semantic Segmentation Model for Mars Helicopter

作者：Boyu Lin, Fei Wang, Q. X. Li, Bo Zheng, Meibao Yao, Xueming Xiao, Yifan Qi, Hutao Cui, Xiang‐Yu Huang · 发表于：Aerospace · 年份：2025 · DOI：10.3390/aerospace12121049 · 被引用次数：4 · 研究领域：Planetary Science and Exploration、Advanced Neural Network Applications、Advanced Image and Video Retrieval Techniques

With the continuous deepening of Mars exploration missions, the Mars helicopter has become a key platform for acquiring high-resolution near-ground imagery. However, accurate semantic segmentation of the Martian surface remains challenging due to complex terrain morphology, sandstorm interference, and the limited onboard computational resources that restrict real-time processing. Existing models either introduce high computational overhead unsuitable for deployment on Mars aerial platforms or fail to jointly capture fine-grained local texture and global contextual structure information. To address these limitations, we propose LisseMars, a lightweight semantic segmentation network designed for efficient onboard perception. The model integrates a Window Movable Attention (WMA) module for enhanced global context extraction and a multi-convolutional feedforward module (CFFN) to strengthen local detail representation. A Dynamic Polygon Convolution (DPC) module is further introduced to improve segmentation performance on geometrically heterogeneous objects, while a Group Fusion Module (GFM) enables effective multi-scale semantic integration. Extensive experiments are conducted on both real Tianwen-1 Mars helicopter imagery and synthetic datasets. The results show that our method achieved a mean IoU of 78.56% with only 0.12 MB of model parameters, validating the effectiveness of the proposed framework. The real-time performance of proposed method on edge device deployment further d...