基于改进DeepLab-v3+的火星地形分割算法

Mars Terrain Segmentation Algorithm Based on Improved DeepLab-v3+

准确的地形分割有助于星球巡视器执行地形可通过性判断、自主路径规划等任务,从而保证巡视器探测任务的顺利进行.针对当前火星地形分割任务难度高、巡视器计算资源有限的问题,基于DeepLab-v3+网络结构提出一种轻量化的语义分割网络.该网络以MobileNetV2为骨干网络,利用密集连接的方式优化空洞空间金字塔池化(ASPP)模块,进一步扩大了空洞卷积的感受野;融入最新提出的坐标注意力机制(CA),增强了网络的特征提取能力.利用AI4Mars公开数据集对CA-DeepLab-v3+网络进行验证,表明算法在土壤、基岩、沙地和大岩石4种地形的召回率分别达到91%、92%、89%和75%.

lanetary rover systems need to perform terrain segmentation to identify drivable areas and plan the path,so as to ensure the success of rover detection missions.At present,the task of Mars terrain segmentation is difficult and the computational resources of the rover are limited.This paper proposes a lightweight semantic segmentation network based on DeepLab-v3+network structure.The backbone network is MobileNetV2.The Atrous spatial Pyramid pooling(ASPP)module is optimized by dense connection to further expand the receptive field of the atrous convolution.The coordinate attention(CA)mechanism proposed recently is used to increase the feature extraction ability of our network.CA-DeepLab-v3+network is verified by AI4Mars public dataset,which shows that the recall rate of the algorithm can reach 91%,92%,89%and 75%in soil,bedrock,sand and large rock,respectively.