RockSeg: A Novel Semantic Segmentation Network Based on a Hybrid Framework Combining a Convolutional Neural Network and Transformer for Deep Space Rock Images

Rock detection on the surface of celestial bodies is critical in the deep space environment for obstacle avoidance and path planning of space probes. However, in the remote and complex deep environment, rocks have the characteristics of irregular shape, being similar to the background, sparse pixel characteristics, and being easy for light and dust to affect. Most existing methods face significant challenges to attain high accuracy and low computational complexity in rock detection. In this paper, we propose a novel semantic segmentation network based on a hybrid framework combining CNN and transformer for deep space rock images, namely RockSeg. The network includes a multiscale low-level feature fusion (MSF) module and an efficient backbone network for feature extraction to achieve the effective segmentation of the rocks. Firstly, in the network encoder, we propose a new backbone network (Resnet-T) that combines the part of the Resnet backbone and the transformer block with a multi-headed attention mechanism to capture the global context information. Additionally, a simple and efficient multiscale feature fusion module is designed to fuse low-level features at different scales to generate richer and more detailed feature maps. In the network decoder, these feature maps are integrated with the output feature maps to obtain more precise semantic segmentation results. Finally, we conduct experiments on two deep space rock datasets: the MoonData and MarsData datasets. The experimental results demonstrate that the proposed model outperforms state-of-the-art rock detection algorithms under the conditions of low computational complexity and fast inference speed.