Accurate detection and segmentation of obstacles is the key to the smooth operation of the planetary rovers and the basic guarantee of scientific exploration mission. The traditional method of rock segmentation based on boundary detector is affected by the change of illumination and dust storms. To address this problem, this paper proposes an improved U-net-based architecture combined with Visual Geometry Group (VGG) and dilated convolutional neural network for the segmentation of rocks from images of planetary exploration rovers. The proposed method also has a contracting path and an expansive path to get high-resolution output similar with U-Net. In the contracting path, the convolution layers in U-Net are replaced by the convolutional layers of VGG16. Inspired by the dilated convolution, the multiscale dilated convolution in the expansive path is proposed. Furthermore, our method is further optimized in the expansive path. To evaluate the proposed method, extensive experiments on segmentation with the Mars dataset have been conducted. The experimental results demonstrate that the proposed method produces accurate semantic segmentation and identification results automatically and outperforms state-of-the-art methods.

准确检测和分割障碍物是行星探测器顺利运行的关键，是科学探索任务的基本保障。传统的基于边界检测器的岩石分割方法受光照和沙尘暴变化的影响。为了解决这个问题，本文提出了一种改进的基于 U-net 的架构，结合视觉几何组 (VGG) 和扩张卷积神经网络，用于从行星探测车的图像中分割岩石。所提出的方法还具有收缩路径和扩展路径，以获得类似于 U-Net 的高分辨率输出。在收缩路径中，U-Net 中的卷积层被 VGG16 的卷积层代替。受扩张卷积的启发，提出了扩展路径中的多尺度扩张卷积。此外，我们的方法在扩展路径中得到了进一步优化。为了评估所提出的方法，已经对火星数据集进行了大量的分割实验。实验结果表明，所提出的方法自动产生准确的语义分割和识别结果，并且优于最先进的方法。