MarsSeg: Mars Surface Semantic Segmentation with Multi-level Extractor and Connector

• URL: http://arxiv.org/abs/2404.04155v1
• Date: Fri, 5 Apr 2024 15:04:57 GMT
• Title: MarsSeg: Mars Surface Semantic Segmentation with Multi-level Extractor and Connector
• Authors: Junbo Li, Keyan Chen, Gengju Tian, Lu Li, Zhenwei Shi,
• Abstract summary: We propose a novel encoder-decoder based Mars segmentation network, termed MarsSeg. The Mini-ASPP and PSA are specifically designed for shadow feature enhancement. The SPPM is employed for deep feature enhancement, facilitating the extraction of high-level semantic category-related information.
• Score: 19.053126804261034
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The segmentation and interpretation of the Martian surface play a pivotal role in Mars exploration, providing essential data for the trajectory planning and obstacle avoidance of rovers. However, the complex topography, similar surface features, and the lack of extensive annotated data pose significant challenges to the high-precision semantic segmentation of the Martian surface. To address these challenges, we propose a novel encoder-decoder based Mars segmentation network, termed MarsSeg. Specifically, we employ an encoder-decoder structure with a minimized number of down-sampling layers to preserve local details. To facilitate a high-level semantic understanding across the shadow multi-level feature maps, we introduce a feature enhancement connection layer situated between the encoder and decoder. This layer incorporates Mini Atrous Spatial Pyramid Pooling (Mini-ASPP), Polarized Self-Attention (PSA), and Strip Pyramid Pooling Module (SPPM). The Mini-ASPP and PSA are specifically designed for shadow feature enhancement, thereby enabling the expression of local details and small objects. Conversely, the SPPM is employed for deep feature enhancement, facilitating the extraction of high-level semantic category-related information. Experimental results derived from the Mars-Seg and AI4Mars datasets substantiate that the proposed MarsSeg outperforms other state-of-the-art methods in segmentation performance, validating the efficacy of each proposed component.

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