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<p>Rock segmentation on the Martian is particularly critical for rover navigation, obstacle avoidance, and scientific target detection. We propose a lightweight network for real-time semantic segmentation of Martian rocks (RockNet). First, we propose the cross-dimension channel attention (CDCA) model to replace traditional downsample and upsample operation, which gives more weight to the channels with more useful information by adjusting the weight of each channel. Second, we modify the short-term dense concatenate model, we adopt dilated convolution to learn the feature with a larger receptive field, and through the skip connection structure, the degradation of the network can be reduced. Finally, we propose a feature fusion module (FFM) to fully fuse different levels of features. With only 0.86M parameters, our model gets 82.37% mIoU and 105.7 FPS running speed on the dataset of TWMARS.</p>

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Rocknet: lightweight network for real-time segmentation of Martian rocks

  • 期刊论文

  • 发表日期：

    2025年1月10日

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  • Pengfei Wei,
  • Zezhou Sun,
  • He Tian

摘要

Rock segmentation on the Martian is particularly critical for rover navigation, obstacle avoidance, and scientific target detection. We propose a lightweight network for real-time semantic segmentation of Martian rocks (RockNet). First, we propose the cross-dimension channel attention (CDCA) model to replace traditional downsample and upsample operation, which gives more weight to the channels with more useful information by adjusting the weight of each channel. Second, we modify the short-term dense concatenate model, we adopt dilated convolution to learn the feature with a larger receptive field, and through the skip connection structure, the degradation of the network can be reduced. Finally, we propose a feature fusion module (FFM) to fully fuse different levels of features. With only 0.86M parameters, our model gets 82.37% mIoU and 105.7 FPS running speed on the dataset of TWMARS.